{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import data\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/pycaret/pycaret/master/datasets/automobile.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration',\n",
       "       'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',\n",
       "       'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',\n",
       "       'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',\n",
       "       'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg',\n",
       "       'highway-mpg', 'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>-1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.4</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.7</td>\n",
       "      <td>92</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>9988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1</td>\n",
       "      <td>128.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>idi</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.47</td>\n",
       "      <td>21.9</td>\n",
       "      <td>55</td>\n",
       "      <td>4800</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>7099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>18920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>103.5</td>\n",
       "      <td>...</td>\n",
       "      <td>209</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.0</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>30760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>100.4</td>\n",
       "      <td>...</td>\n",
       "      <td>181</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.27</td>\n",
       "      <td>9.0</td>\n",
       "      <td>152</td>\n",
       "      <td>5200</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>13499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling  normalized-losses    make fuel-type aspiration num-of-doors  \\\n",
       "171         -1               65.0  toyota       gas        std         four   \n",
       "87           1              128.0  nissan    diesel        std          two   \n",
       "7            1                NaN    audi       gas        std         four   \n",
       "14           0                NaN     bmw       gas        std         four   \n",
       "98           0              128.0  nissan       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "171  hatchback          fwd           front       102.4  ...          122   \n",
       "87       sedan          fwd           front        94.5  ...          103   \n",
       "7        wagon          fwd           front       105.8  ...          136   \n",
       "14       sedan          rwd           front       103.5  ...          209   \n",
       "98       sedan          fwd           front       100.4  ...          181   \n",
       "\n",
       "     fuel-system  bore  stroke compression-ratio horsepower  peak-rpm  \\\n",
       "171         mpfi  3.31    3.54               8.7         92      4200   \n",
       "87           idi  2.99    3.47              21.9         55      4800   \n",
       "7           mpfi  3.19     3.4               8.5        110      5500   \n",
       "14          mpfi  3.62    3.39               8.0        182      5400   \n",
       "98          mpfi  3.43    3.27               9.0        152      5200   \n",
       "\n",
       "    city-mpg highway-mpg  price  \n",
       "171       27          32   9988  \n",
       "87        45          50   7099  \n",
       "7         19          25  18920  \n",
       "14        16          22  30760  \n",
       "98        17          22  13499  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(202, 26)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove rows with '?'\n",
    "df = df[df['bore']!='?']\n",
    "df = df[df['stroke']!='?']\n",
    "df = df[df['horsepower']!='?']\n",
    "df = df[df['peak-rpm']!='?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert data type\n",
    "df['bore'] = df['bore'].astype('float')\n",
    "df['stroke'] = df['stroke'].astype('float')\n",
    "df['horsepower'] = df['horsepower'].astype('int')\n",
    "df['peak-rpm'] = df['peak-rpm'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = df.select_dtypes(include='O').columns.to_list()\n",
    "df[cat_cols] = df[cat_cols].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection strategies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unused columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df.drop(columns=['Id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling             0\n",
       "normalized-losses    35\n",
       "make                  0\n",
       "fuel-type             0\n",
       "aspiration            0\n",
       "num-of-doors          2\n",
       "body-style            0\n",
       "drive-wheels          0\n",
       "engine-location       0\n",
       "wheel-base            0\n",
       "length                0\n",
       "width                 0\n",
       "height                0\n",
       "curb-weight           0\n",
       "engine-type           0\n",
       "num-of-cylinders      0\n",
       "engine-size           0\n",
       "fuel-system           0\n",
       "bore                  0\n",
       "stroke                0\n",
       "compression-ratio     0\n",
       "horsepower            0\n",
       "peak-rpm              0\n",
       "city-mpg              0\n",
       "highway-mpg           0\n",
       "price                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total null values per column\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop column\n",
    "df = df.drop(['normalized-losses'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remaining number of columns\n",
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncorrelated features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Numeric features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x184b91972e0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation between y and features\n",
    "(df.corr().loc['price']\n",
    " .plot(kind='barh', figsize=(4,8))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop numeric features  not correlation with the target (threshold <0.2)\n",
    "corr = abs(df.corr().loc['price'])\n",
    "corr = corr[corr<0.2]\n",
    "cols_to_drop = corr.index.to_list()\n",
    "df = df.drop(cols_to_drop, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f7010cff10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y = 'price', x = 'fuel-type', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Low varience features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wheel-base       37.94025405546827\n",
       "length           155.9424623233909\n",
       "width            4.570510465724747\n",
       "curb-weight      275205.6007064364\n",
       "engine-size      1708.835871271584\n",
       "bore           0.07456791993720567\n",
       "horsepower      1427.3492412349553\n",
       "city-mpg        40.974856096284675\n",
       "highway-mpg      46.57443746729464\n",
       "price            65020222.64615383\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variance of numeric features\n",
    "(df\n",
    " .select_dtypes(include=np.number)\n",
    " .var()\n",
    " .astype('str'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    196.000000\n",
       "mean       3.331684\n",
       "std        0.273071\n",
       "min        2.540000\n",
       "25%        3.150000\n",
       "50%        3.310000\n",
       "75%        3.590000\n",
       "max        3.940000\n",
       "Name: bore, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bore'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multicolinearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Numeric features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(16,10)})\n",
    "sns.heatmap(df.corr(),\n",
    "            annot=True,\n",
    "            linewidths=.5,\n",
    "            center=0,\n",
    "            cbar=False,\n",
    "            cmap=\"PiYG\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop correlated features\n",
    "df = df.drop(['length', 'width', 'curb-weight', 'engine-size', 'city-mpg'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>body-style</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>diesel</td>\n",
       "      <td>sedan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>gas</td>\n",
       "      <td>convertible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gas</td>\n",
       "      <td>convertible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>gas</td>\n",
       "      <td>wagon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>gas</td>\n",
       "      <td>hatchback</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fuel-type   body-style\n",
       "183    diesel        sedan\n",
       "185       gas  convertible\n",
       "1         gas  convertible\n",
       "110       gas        wagon\n",
       "80        gas    hatchback"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat = df[['fuel-type', 'body-style']]\n",
    "df_cat.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>body-style</th>\n",
       "      <th>convertible</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel-type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>diesel</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gas</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>62</td>\n",
       "      <td>80</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "body-style  convertible  hardtop  hatchback  sedan  wagon\n",
       "fuel-type                                                \n",
       "diesel                0        1          1     15      3\n",
       "gas                   6        7         62     80     21"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab = pd.crosstab(df_cat['fuel-type'], df_cat['body-style'])\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9.205409356725148,\n",
       " 0.05616535232132477,\n",
       " 4,\n",
       " array([[ 0.6122449 ,  0.81632653,  6.42857143,  9.69387755,  2.44897959],\n",
       "        [ 5.3877551 ,  7.18367347, 56.57142857, 85.30612245, 21.55102041]]))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "chi2_contingency(crosstab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop rows with missing values\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get dummies for categorical features\n",
    "df = pd.get_dummies(df, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X features\n",
    "X = df.drop('price', axis=1)\n",
    "\n",
    "# y target\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into training and testing set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert back to dataframe\n",
    "X_train = pd.DataFrame(X_train, columns = X.columns.to_list())\n",
    "X_test = pd.DataFrame(X_test, columns = X.columns.to_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate model\n",
    "model = LinearRegression() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f701996310>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nNTWVNWvWYGxszOjRo2nbti0REREEBwcTERGBl1fZO9G9OBUSFBTEyJEj5f9e2knwUDQtEhMTQ/PmzZk+fTo//fQTxsbGr9Ps5T7dVoT6PDqracpU1/qspn4OIvJeCa9zmrq1tTVTpkxh5MiRjB49GhMTk/8fhV4m7/zs7e1RU1PD3NycSZMmYWtry8yZM9HX1+fGjRvya50+fZrk5GR5R/vs2TMMDAzo378/enp69OzZE4B27doxePBgAAwMDMjIqHh3OplMxrp160hKSiIsLEz+emknwUPRnH7r1q0BGDNmDImJia/dcb+8HFCRlGmqRFnq2hB+OdXEz0FE3l9SfKp6scLCQtTV1ZFIJFU+Td3Hx4fLly9z4sQJPDw8cHd3x97engkTJhAXF0d8fDzbt28HYPPmzVy4cIGTJ08ye/ZsPvvssxLXlkgkTJ8+nZkzZwLw6NEj1NTUyMnJeWVO/sWIe0UKCwtZsmQJmZmZhIWF0aLF//7DL+0keCgZ1y+O0AuC0Dg0yP+btbW1efDgAdnZ2TRv3pyEhASsra0xMzOr0mnqhYWFjBo1ivDwcFxdXSkoKCAlJQV7e3scHR2ZOnUq3bp1Q19fn+zsbN555x327t2Lubk5d+/eJTU1FRMTE/l+JZaWlgQFBeHk5ISWlhZubm44ODhgYWFRrfoW7/K3Y8eOV34BlHYS/PHjxzlx4gQLFixAS0uLQ4cOMX/+/GqVQRBqSn2PvNf3uDs00I67RYsWzJ49m0mTJtGuXTv69OkDQJs2bap0mrq6ujrz58/nvffeQ0tLC11dXXkn3759e9q3by+Pobdu3RpnZ2cmTZpE06ZN6dy5MxMnTiQ/P5/c3Fw8PDwIDAzk8uXLODk5IZFIGD58OA4ODty+ffu165qdnc3OnTsxMjLi7bfflr9evFlVaSfBA3Tp0oW5c+fy6NEjxo0bx7Bhwyo1JSMItU1E3qtPRN5LIZPJuHfvHi4uLhw8eLDeLD2sK2KOWzGUpa6inhVrlHPcte3w4cP4+fnh5+ensE574cKFXLt27ZXXbWxsWLBggULKIAhCwyBG3EKFxIhbMZSlrqKeFWt0m0x5enpia2vLwYMHq/Q9GxubWpnjTU9PL3cNdl2TSCTMmjWL0aNHs337dry9veu6SIIgVFODmypRxsh7dWRmZpKamsrPP/9c10URBKB+RN4bQqy9PA2q41bWyLuLiws9e/bk3Llz5Ofns2jRIsLCwvjrr7+YMWMGM2bMYNOmTdy5c4e//vqLnJwcnJ2dmT17Nq6urjx48ABHR0eWLFlCcHBwg9uNUGhc6kPkvSHE2svToKZKioM1+/fvR1dX95X3iyPv27dvZ//+/QwbNuyVkMzLNmzYwI4dO4iKisLQ0FAeeS9ebvdi5H3r1q3s27ePqKgoCgoK5JH33r17vxJ5379/P5mZmfLNoFJTU5kzZw4xMTGcP3+e27dvExERwbhx44iIiKiw7jKZjL179zJ69GgCAgIIDg5m586dhISEyD9z6dIlvv76a6KiooiIiOCPP/7gyy+/pG3btvJTdARBaPga1Ii7Io058m5lZSX/vJmZGU2bNsXQ0LDE4cXjxo2jWbNmQNGc/q+//sro0aMr2XplE3uVKI4y1bWuKaKtxV4lL1C2yDsgj7IDZcbXX46/V/UeZRGrShRDWepaX3451XZbi3XcL1G2yHtlHTlyhGnTpvH06VOOHTsm/wUmCPVJfYi8N4RYe3kaZMetTJH3qtDS0mLq1Kk8fvwYV1dXunXrJmLuQr0jIu/VJwI4pWiIkfcXV9bUNDFVohjKUldRz4o1yqmS2iYi74Ig1GdixC1USIy4FUNZ6irqWbFGF3mvaRs3buSnn36q9Odzc3Nxc3MDilKJc+bMqdHyvHj9yjpz5gwuLi41Wg5BEOovpZ8qqeo0xMOHD0lJSQFAX1+fr776qkbL8+L1BaExqovIe0OPuL+sQXfchYWF+Pn5cfXqVbKysujRowdr165l4cKFZGVlAeDm5oatrS0uLi4YGxtz9uxZ8vPz8fLyYtiwYSxduhQLCwssLCyYPXs2Ojo6NGnShE2bNuHl5UVmZib37t1j8ODBrFq1ioCAAO7du4ebmxuenp5MmzaNo0ePkpWVhbe3N3fu3EFdXZ2PP/4YKysrNm3aRGZmJjdv3uT27du8/fbbzJs3r8w6lXV9KPkA0tLSkt69e/PPP/+wePFicnJymDVrFvfu3cPU1BRfX180NTU5duwYGzZsQCqV0qFDB/z9/dHT06v9H44glKEuIu8NPeL+sgY9VfL777+joaFBREQEP/74I7m5uRw5cgRDQ0OioqJYtWoVZ8+elX/+8ePHREdHs379epYuXcrz589LXC8tLY3AwEC+/vprjh8/jomJCRERERw+fJjffvuNP/74Ax8fH9q2bVsiag6wcuVKLC0tiY2NJSgoCC8vL/kvj9TUVLZv386ePXsIDQ0tkXZ8WVnXf1lOTo48Qq+urk5GRgbLli3jwIEDPHnyhF27dnH//n2WL19OSEgIsbGx9OvXD39//6o2syAI9UyDHnEPHDiQVq1asXPnTq5fv86NGze4ffs2R44cITMzkxEjRpSYLy7eNMrExIQ2bdqQmppa4nq6uroYGRkBRfHx5ORkvvnmG65fv86DBw/Iy8ujVatWpZbl119/JSAgAIAOHTpgZmYm3wRr0KBBaGpqoqurS6tWrcjNzUVbW7va9TczM5P/ecCAAXTq1AkAOzs7oqKi6NixI6ampvI6OTs7ExoaWuX7iMi74ihTXRWtLtpWRN5L8dNPPxEUFMS0adNwdHQkJycHAwMD4uPjSUhI4NixY+zYsYNDhw4Br0bCX46ON2nSRP7n8PBwDh8+jJOTE0OGDOHKlSuUtwDn5fdkMhkSiQQoCsYUezmuX56yov2llffF12UyGerq6kil0lfKVJzyrAqxqkQxlKWudfXLSdFtK9Zxl+H06dOMGTOGiRMnkp6ezpkzZzAzM2PTpk14enpiZWWFtbU1jx8/BuDQoUOYmppy8eJFHj16RPfu3cu89qlTp3B2dsbOzo6LFy9y+fJleWdfWudnaWnJ3r17mTlzJunp6Zw/fx4/P79XRvUVefH6ZUX7S3Pu3Dnu3LlDu3bt2L9/P8OHD8fMzAxfX18yMjIwMjIiIiKCQYMGVak8glDT6iLy3tAj7i9r0B3322+/zaJFi4iLi0NDQ4N+/fpx584d0tLSsLOzQ01NDQ8PD/m0RHp6ujzC/sUXX5S7CdP06dPx8/MjNDSU5s2bY25uTkZGBgMGDMDAwAAXFxfWrFkj/7y3tzfLly+Xb58aEBBA27Ztq1wnXV1d+fXDw8NLjfaXplu3bnh5efHPP/9gaWnJpEmTUFNTw9/fH3d3dwoKCjAwMCixV4sg1AURea8+pQnguLi44O7uLkacr0FMlSiGstRV1LNijXqqpKE6e/YsK1euLPW90NBQ9PX1FVwiQRAaEqUZcQuvT4y4FUNZ6irqWTERea8lNXFqfG1E5gVBaPzEVEkdqo3IvCDUd4qOvDe2uDsoecd95swZtmzZgoaGBhkZGdjY2PDGG29w5MgRoGi++YcffiAmJoanT5+ioaHB+vXr6dKli/waaWlpuLq6sm7dOvr06cO6detITExEIpHg6OjIjBkzyrx/RkaGPNJ+5coVVq5cSV5eHtnZ2cydO5cpU6aQm5vL4sWLuXXrFh06dODu3bsEBweTmJhIYmKi/OCH4oev/fv3f2UbgM8//5ysrKwSkf6vv/66VttWEMqi6Mh7Y4u7g5gqISkpiRUrVrBv3z527txJ69atiYqKokePHsTFxXHkyBHCw8M5ePAgI0aMYOfOnfLv3r17F3d3d1avXk3fvn2JjIwEIDo6mr179/LTTz+ViNyXZ8+ePXzwwQfs27ePsLAw1q1bB0BISAidO3cmLi4ONzc3rly5Uu51StsG4MSJE0DJSL8gCA2XUo+4Abp370779u0B0NHRKXH6+qNHj1i/fj1xcXHcuHGDhIQETExM5N9dsGABffr0YcCAAUBRICglJYVff/0VgLy8PFJTU+Xvl2fp0qUkJCSwdetWrly5Ql5eHlAUBCo+lLhPnz7lhoag9G0Aiq/1YqS/KkTkXXGUqa6KVFftKiLvteTF09OhZCz+77//xtnZmXfffRcrKyv09PRKbLnq7e1NSEgIx48fZ8SIEUgkEjw8PBg1ahQA2dnZNGvWrFLl+Oijj9DW1sba2pqxY8dy8OBBeXlKW/jzchy+oKAAKH0bgOLPvRiRrwqxqkQxlKWuddGJ1kW7inXcdeTixYu8+eabzJgxg2fPnhEUFES7du3k75uamuLn58eSJUuwsLDA0tKSyMhIrK2tef78OVOnTmXFihWVCv2cOnWK+Ph49PX15dMxEomEwYMHExsbi7GxMampqVy9ehUVFRV0dHT466+/kMlkZGRkyKP1pW0DUPy3CEGoDxQdeW9scXcQHXe5hg0bxuXLlxk7diwymYyBAwdy9erVEp8ZOHAggwYNYsOGDXh4eHDz5k0cHBwoLCzE0dGx0knNDz/8kKlTp6KlpYWxsTGGhoZkZGTI9+W2s7OjY8eO6Onp0aRJE4YMGcK+ffv4z3/+Q+fOnenfvz9Q+jYA4qR3oT4RkffqEwGcOnT58mUWLVoknxYpTUxMDEZGRvTv3587d+7w7rvvcuTIEVRVFfdcWUyVKIay1FXUs2JiqqSO3bp1iw8//PCV169du4ZMJmPhwoXlfr9Lly74+voilUpRVVXF399foZ22IAj1jxhxCxUSI27FUJa6inpWTETeBUEQGpl6MVViY2NDWFjYa60xro9qagvZHj16VPkgBkGo72oy8t4Y4+yVUS86bkEQlEdNRt4bY5y9Mmqs464P+364ubnRpUsXrl27Rs+ePTE3Nyc6OpqHDx8SEhJC165dSU5OZs2aNTx79gwdHR1WrFhBhw4dcHFxoWXLlly9epUNGzZw7do1vvzyS1RUVOjTpw8rV67k+fPn+Pv7c/XqVSQSCXPmzGHcuHE8f/4cb29vLl26hKGhITk5OfJyhYaGEh8fj0QiYdiwYXh4ePDkyRM++eQT+Snwbm5u2NrallqvZcuWkZycjI6ODqtXr8bAwICbN2/i5+fHgwcPaNKkCcuWLaNnz54sXbqU5s2b88cff5CZmYmbmxsTJ05k8eLF8pF7dnY2LVu2LHcliyAI9VuNjriTkpKIi4ujVatWDBkyhCVLlhAVFYWnpydxcXEcO3aM8PBwmjRpwsaNG9m5cyfLli0Divb98PX1le/7sWvXLqBo34/nz58za9YsevfuXW58PDU1lTVr1mBsbMzo0aNp27YtERERBAcHExERwaJFi/Dx8WHLli0YGBiQkJDAsmXL+Oabb4CiqYng4GAyMzNZs2YNUVFRtGvXDg8PD06cOMGFCxfo1asXa9eu5fHjx0yePBkzMzP+7//+D4D4+Hhu3LjB+PHjATh58iSXLl1i7969qKio4OHhwYEDB5BKpRgaGhIaGkpKSgoHDhwos+MeOHAgK1euZOfOnaxatYqQkBCWLFnC8uXL6dmzJ9euXcPNzY3Dhw/L2/H777/nypUrTJs2jYkTJ8r3PXnw4AFTpkxhxYoVVfq5isi74ihTXWtKfW6zBhF5r+t9P/T09OjZsycA7dq1K3H/jIwMbty4QXp6OvPmzZN/p/ggYShKQkLRRk39+vWTpyQDAwMB2Lx5M8+ePWPfvn3yMl29epXExEScnZ0B6NSpE+bm5vI6JCcn4+joCMCzZ88wMDBg4sSJfP7552RmZjJixAjc3NxKrU+TJk3kvwTs7e3ZsGEDT5484dKlS3h6eso/l5eXJx/lDx06FBUVFbp3786DBw/knyksLGTBggVMmzZNHtapLLGqRDGUpa413ZnV1zZrMOu463rfD01NzTLvDyCVSjEyMiImpmh+TSKRyKcr4H97eairq6OioiJ/PTs7W/79wMBAevXqBUBWVhYtW7YkMjKyxL4h6urq8utPnz6dmTNnAvDo0SPU1NRo1qwZ8fHxJCQkcOzYMXbs2MG3337L3LlzAWjbti1fffVVifXaMpkMdXV1pFIpmpqa8jpA0Si7VatWAGhpabEeM9AAACAASURBVAGUKD/A6tWr6dChA1OmTCm3DQVBqP8U9nBSkft+lKVLly48fPiQs2fPMmDAAPbt20dsbCzh4eElPtenTx9WrFjBP//8Q5s2bVi9ejWDBg3C0tKSXbt2ERAQwL1795gwYQK7d++W7ydibW3N33//zfnz5wGwtLQkKCgIJycntLS0cHNzw8HBgby8PNLT0/H09MTKygpra2uaNm1aojOGopH0Tz/9hK2tLfv27WPIkCG0aNGCTp06ERMTg729PadOnWL58uXyZwmliYyM5M8//yQsLOy1204QakpN7lXSGPchqQyFddyK3PejLJqammzcuJFVq1aRn59P8+bNWbt27Suf09fXx9vbm1mzZiGVSunbty+Ojo48ffoUPz8/xo0bJ/8bQceOHZk6dSpXr15lzJgxGBoayrdetbGx4fLlyzg5OSGRSBg+fDgODg7yh5N2dnaoqanh4eGBtrb2K+XQ1tbmyJEjbNy4EX19fdasWQMUTd34+fmxbds2NDQ0+OKLL14ZYb/I39+fDh064OzsjFQqBSAiIuK1dwsUhOoQe5VUn0hOChUSc9yKoSx1FfWsWKPaq6SsfT8AAgIC6NOnj4JLJAiCoHgNquPu2LHjK/PAgiAIyqZBddwNSU3E+F88TFgQGgsRea8+0XELgqBQIvJefaLjLkddx/gB8vPzWbBgAWlpaXTs2JFVq1bRsmVLbGxs+O9//8upU6dQV1fngw8+YMeOHdy8eZMlS5bQsWNHVqxYwZ49e8jLy8PCwoKdO3diZmbG8uXLGTx4MGPGjKnN5hMEoZaIjrsCdR3jv3//Pi4uLgwYMIC1a9cSEhKCl5cXUJQULS5LaGgoYWFhnD9/ntWrVxMdHc29e/fIzc3l999/R1tbm8TERMzMzPj1119ZvHhxpdtARN4VR5nqWlPqc5s1iMh7Y1TXMf7OnTvL37e3t2fp0qXy96ysrORladu2Lerq6vJyqaioMGTIEM6cOcP58+eZPn06v/32G9bW1rRv357mzSvfGYvlgIqhLHUVkfeKiYMUqqkyMf7c3FysrKxwcHAoEX339vYmPT2d48ePA8hDOzExMcTExBAREcGkSZPKvX9xfB7+F3svrWwvvl5sxIgRnD59mnPnzjF16lSuXbvGsWPHsLa2rlzlBUGol8SIuxoUEeP/66+/+PPPP+nZs6c89l5ZQ4cO5fPPP6d169a0aNECExMTwsLCXon4C4Iiich79YmOuxoUEePv2LEjISEh3Lp1i+7du/Pxxx9XunzNmzenXbt28mCSpaUl165do1OnTlWuqyDUFBF5rz4ReRcqJOa4FUNZ6irqWbFGFXlvjESMXxCEqhIddxkUdYBxTcX4z5w5Q3BwsJi/FgQlIDpuQRAUSkTeq69Rd9x1nXzMyMhg3rx58gOMDQwMCAwMpFWrVhw7dowNGzYglUrp0KED/v7+6OnpYWNjg6mpKSkpKXz99df4+fm9cqhwSkoKy5cv59mzZ7Rs2ZLPPvsMKDqpZ86cOdy6dYvOnTsTFBSEpqYm+/fv59tvv0UqldKrVy98fX3lJ+UIgqKJyHv1Nfp13ElJSaxYsYJ9+/axc+dOWrduTVRUFD169CAuLo4jR44QHh7OwYMHGTFiBDt37pR/9+7du7i7u8uTj5GRkUBR8nHv3r389NNPnD17ttz7X7lyhalTpxIXF0fXrl0JDg7m/v37LF++nJCQEGJjY+nXrx/+/v7y71hZWXH48GHOnDmDoaEhUVFRrFq1Sn6vRYsW8cEHHxAbG8vYsWP59ttvAbhz5w7Lly8nPj6erKwsfvnlF65evUpkZCS7d+8mJiYGXV1dtm/fXtPNLAiCAjXqETfUffKxU6dO8iV/EyZMYNGiRQwdOhRTU1P5/LmzszOhoaHy75iZmQFgbm7+yqHC2dnZ/PPPP/IQzdSpU4Giv10YGxvToUMHALp27UpOTg4ZGRncvHkTJycnAAoKCuQHKleWiLwrjjLVtabU5zYTkffXVNcHGL+cfFRTU5MfH/bi64WF/wsSFE9jdOrU6ZVDhffs2VPimLL8/Hzu3bv3yr1UVFSQyWRIJBLGjBmDj48PAE+ePEEikZRb5peJ5YCKoSx1FZH3ionIezleTD726dOHI0eOlOjUipOP/v7+5OXlyZOPBQUFPHnyhKlTp3LhwoVy75GWlib/ZbBv3z6srKwwMzMjKSmJjIwMoOj8x9KCON999x2bNm1izJgx+Pr6kp2djUwmQ19fn59//hmAmJgYNm7cWOb9Bw0axI8//sj9+/eRyWT4+fnJp1YEQWiYGv2IuzyKSD62bNmSoKAgbt26RY8ePQgICOCNN97A398fd3d3CgoKMDAwYNWqVa98d8KECaUeKlx8WHBgYCA6OjqsW7eOtLS0Uu9vbGyMu7s706dPRyqVYmJiwty5c1+/0QShmkTkvfpEcrIWNZYTbMRUiWIoS11FPSsmkpO1rKLkoyAIQk0THXc1VZR8bOijbUEQ6h+lfjgpCILQEImOu5bZ2NjIV4/UlTNnzuDi4gIULXG8ePFinZZHUG7Fkfeq/tNCu2ldF73eEFMlSqa01SuCoEivG3lX1nh7aUTHXQl1vecJwBdffMHp06d5+PAhbdu25YsvvkBPT48ePXqQmpoKQFRUFImJiXz66af8/PPPrFmzBi0tLTp37iy/jouLC+7u7hUuYxQEof4SHXcl1eVp7zdv3uT69evs3r0bVVVVFi9ezIEDB3jvvfdK/fzz589ZunQp3377LV27dsXb27tadReRd8VRprq+jobWPiLyXsfqcs+TN998kyVLlrBnzx7S0tK4cOECHTt2LLOsqamptG3blq5duwLg4OBQbrqyImIdt2IoS12r05k1pPYRkfd6oC5Pe7906RKzZs1CKpUyevRoRo4cWeL6xX8u3u+keJ+S0soqCELDJ0bcNaC2T3v/7bffsLCwYMqUKeTk5HD8+HH5Rlc6OjpcvXqVf/3rXxw9epRWrVrRo0cPsrKyuHz5MsbGxsTFxSmkHQShMl438q6s8fbSiI67BtT2nidjx47F3d0dOzs7AHr37i1fYrhw4ULef/999PT06N+/Pzk5OWhoaPD555/j4eGBurp6lbdxFYTaJE55rz6xV4lQITHHrRjKUldRz4qJvUoaCHHauyAIlSU67nqipk57FwSh8WuQq0o8PT2xtbXl4MGDVfpebcXP09PT8fLyqvHr1pSlS5cSFRVFZmYmc+bMqeviCEpORN6rr0GOuKOjo0lOTkZTU7OuiwIUHdKbnp5e18WokL6+Pl999VVdF0NQciLyXn0NruN+//33kclkDBkyhIKCApKSkgDYtGkTAB9++CEnT54kKCiIwsJCjIyMWLlyJTo6OmVeMzY2lm3btqGmpoaRkRGBgYH4+PgwcOBA+SG7Li4uLFq0iPPnzxMdHY2qqiqmpqb4+/sTEBBARkYGK1aswNfXl9DQUOLj45FIJAwbNgwPDw9u376Nm5sbXbp04dq1a/Ts2RNzc3Oio6N5+PAhISEh8sBMab7++utX7hsVFcXx48e5f/++/ADhpUuXAvDpp59y/Phx2rZti0QiwcLCotEc7CAIyq7BddxbtmyhR48e7N+/n2nTpr3yfnZ2NuvXrycsLIyWLVuye/duPvvss3I3V9qwYQORkZHo6uqydu1arl+/zsSJE9m0aRNOTk7cvn2b7OxsevfujaurKwkJCaipqeHt7U1mZiY+Pj4EBwfj6+vLyZMnuXTpEnv37kVFRQUPDw8OHDhA//79SU1NZc2aNRgbGzN69Gjatm1LREQEwcHBRERElDndIpFI2Lp16yv3BTh37hwxMTFoa2szbdo0fvzxR6RSKX/++ScHDx4kNzeX8ePHV6vNReRdcZSprq+jobWPiLxXUlJSEn///be8U5dKpbRs2bLc71hbWzNlyhRGjhzJ6NGjMTExQSaTsWzZMjIyMoiJicHe3h41NTXMzc2ZNGkStra2zJw5E319fW7cuCG/1unTp0lOTsbR0RGAZ8+eYWBgQP/+/dHT05OvqW7Xrl2J2Hx5c+9l3RfA1tYWPT09oGi9d3GMftSoUWhoaNC6dWusrKxeoyX/RywHVAxlqauIvFes0S4HfDnWXVhYiLq6OhKJhH79+rFlyxYA8vPzefLkSYnvent7c+nSJaBoqZ2Pjw+XL1/mxIkTeHh44O7ujr29PRMmTCAuLo74+Hi2b98OwObNm7lw4QInT55k9uzZfPbZZyWuLZFImD59OjNnzgTg0aNHqKmpkZOT88qcfFWi6GXd98VrSKVS1NTUkEqlJdpGXb3B/pgFQShFg/0/WltbmwcPHpCdnU3z5s1JSEjA2toaMzMzfHx8SEtLo3PnzmzevJnMzEw+/fRT+XdfnDYpLCxk1KhRhIeH4+rqSkFBASkpKdjb2+Po6MjUqVPp1q0b+vr6ZGdn884777B3717Mzc25e/cuqampmJiYyPcJsbS0JCgoCCcnJ7S0tHBzc8PBwQELC4vXrmtZ923WrBkJCQnk5uaiqalJXFwc8+fPJz8/n+3btzN58mSePn1KQkICffv2ff3GFoQaJCLv1ddgO+4WLVowe/ZsJk2aRLt27eQBlTZt2rB69Wo++ugjpFIp+vr6BAYGlnkddXV15s+fz3vvvYeWlha6urryTr59+/a0b98eBwcHAFq3bo2zszOTJk2iadOmdO7cmYkTJ5Kfn09ubi4eHh4EBgZy+fJlnJyckEgkDB8+HAcHB27fvv3adS3rvj/88AOtW7dmzpw55OTkMH78eIYPHw4U7Z8ybtw49PT0yn3oKQiKJiLv1Sci72WQyWTcu3cPFxcXDh48WG+WHr7oxYMTapOY41YMZamrqGfFGu0cd207fPgwfn5++Pn5KazTXrhwIdeuXXvldRsbGxYsWKCQMgiCUP+JEbdQITHiVgxlqauoZ8XEQQqlePHU82KViYNv2rRJHvQRBEGoK2Kq5P8TcXBBUIzivUqq6ll+IbmPntZCiRoepe24s7OzmTNnDrdu3aJz584sXryY2bNnc/ToUe7evcuiRYt4+PAh3bt357fffuPkyZMAJCcnM3nyZDIzM3F0dGTevHkMHz6cH3/8kebNmzN58mRsbGyYO3cuBw8e5Ny5cyxcuBAvLy8yMzO5d+8egwcPZtWqVSxevLjUWL2ZmVmJsg4dOhRbW1uSk5PR09Nj4sSJhIeHc/fuXT799FMsLCxwcXHB2NiYs2fPkp+fj5eXF8OGDSu3LoJQF8ReJdWntB33nTt32LJlC4aGhjg5OXH69Gn5e6tWrWLMmDG88847/PjjjyV2Ibx//z67d+/m8ePH2NjYMHPmTCwtLeXHi925c4fffvuNuXPnkpCQwNixYzl+/DgmJiYEBQXx/Plz/vvf//LHH3+UGqt/udMGyMrKwsrKCn9/f1xcXDhy5Ajff/890dHRfPvtt/I14o8fPyY6OpqUlBTmzJnD0aNHy61LZYnIu+IoU11fR0NrHxF5r2HGxsZ06NABgK5du5KTkyN/79SpU6xZswaAf//732hra8vfGz58OJqamrRu3RodHR0ePnzIW2+9xenTp1FVVcXOzo5Dhw5RUFDA2bNn8ff3R0tLi+TkZL755huuX7/OgwcPyMvLY9CgQa/E6stSHFs3NDSkf//+wP9OmC9WPHI3MTGhTZs2pKamlluXyhIPJxVDWeoqIu8VE8sBy/BiDFxFRQUDAwP5v6upqVHWYpuXvyeTybCysuLrr79GTU2NwYMHc/36dfbu3Uv37t3R0tIiPDycw4cP4+TkxJAhQ7hy5QoymQwVFZVXYvWZmZnMnTsXgLZt28rn3V9cklhWVP7l+Lu6unq5dREEoWFS2o67PIMHDyY2NpapU6dy4sSJEqPa0rRu3ZomTZpw7Ngx3N3dSUtLY/PmzfKjyE6dOoWzszN2dnZcvHiRy5cvI5VKAV6J1QOvfRLOoUOHMDU15eLFizx69Iju3btXuS6CUNtE5L36RMddCm9vb5YsWUJkZCTGxsaVml6wsrLixIkTNGvWDEtLS1avXs1bb70FwPTp0/Hz8yM0NJTmzZtjbm4u3w3w5Vh9daSnp8uv88UXX8i3gK1qXQShNonIe/WJAE4pwsLCGDJkCN26deOPP/5g2bJlREVF1fh9ajJW7+Ligru7O4MGDSrxek3URcxxK4ay1FXUs2Jijvs1vPnmm3zyySeoqqqipaXFypUra+U+iojVK6ougiAojhhxCxUSI27FUJa6inpWTETeBUEQGpkG2XEvXbr0lXnan376iY0bN9b6vWtqv5KMjAxsbGxqoERV4+LiwpkzZxR+X0EoVhx5r+o/LbSb1nXR641GM8dta2uLra1tXRdDEIQKiMh79TWIjlsmk/Hpp59y/Phx2rZti0QiwcLCgv/85z/o6OjQpEkT7OzsSExM5N///jd79uyRnzkZHh7OzZs38fT0ZN26dSQmJiKRSHB0dGTGjBkl7pOdnY29vT0JCQlAUUrS09OTsWPHsnXrVnnA5eX9Sj788EMkEkmZ1w8NDSU+Ph6JRMKwYcPw8PAocd/Y2Fi2bduGmpoaRkZGBAYGoqWlVWpbZGRkMHv2bHR0dFBXV+fatWvl7pPi6ekpP2PT0NCwREK0skTkXXGUqa6vo6G1j1JH3g8fPsyff/7JwYMHyc3NZfz48QCkpaWxbds2jIyM5FMnVlZW+Pr68vDhQ1q2bElcXBxeXl5ERkYCEB0dzfPnz5k1axa9e/dmwIAB8vu0bt2a9u3bc+XKFdTU1JBIJCQmJjJ27FgSEhJYsWIFhw4dKnW/ktjY2FKvn5eXx6VLl9i7dy8qKip4eHhw4MABeWwdYMOGDURGRqKrq8vatWu5fv06JiYmZbbHi/X++OOPy90nJTw8HID4+Hhu3Lghb7uqEA8nFUNZ6ioi7xVrFMsBExMTGTVqFBoaGrRu3Vq+b4euri5GRkYlPquhocG///1v/u///o+hQ4fy4MEDTE1N2bZtGykpKfz6668A5OXlkZqaWqLjhqKO//Tp06irqzNt2jTi4uLIzc0lKytLfnZjafuVnD59utTrZ2RkkJycjKOjIwDPnj3DwMCgRMdtbW3NlClTGDlyJKNHjy6303653hXtk/L999/j7OwMQKdOnTA3N3+tn4EgCPVHg+i4i/cEKVa8X0iTJk1K/by9vT0bN27k4cOH2NnZASCRSPDw8GDUqFFA0bRIs2bN2LhxI0ePHgVg/vz5jBgxguDgYDQ1NVmwYAHx8fHExsYybNiwV+7/YtnKuv769euZPn06M2fOBODRo0eoqamVmLLw8fHh8uXLnDhxAg8PD9zd3cvdcOrFele0T0pZbScIdUVE3quvQawqGTx4MPHx8Tx//pyHDx/K56DL0rdvX+7du0dMTIx8asDS0pLIyEgKCgp48uQJU6dO5cKFCyxYsICYmBhiYmKwtbWlV69epKWlcePGDbp27cqgQYP48ssvsba2LveeZV3f0tKSmJgYnjx5QmFhIW5ubhw+fFj+vcLCQkaNGoWOjg6urq7Y29uTkpJS6bZ5cZ+U/v37Y2lpyebNm+XlLd6rRCqVcvv2bc6fP1/pawtCbSiOvFf1H3GIwv80iOHXyJEjuXjxIuPGjUNPT08+ZVGeMWPG8PPPP8u3bp08eTI3b97EwcGBwsJCHB0dX4mHQ9EIun///jx9WvQfiaWlJXv27GHgwIHl3q+861++fBknJyckEgnDhw/HwcGB27dvA0Uj4Pnz5/Pee++hpaWFrq5ulU9tL2+flKlTp3L16lXGjBmDoaEh3bt3r9K1BUGof0RyUqiQeDipGMpSV1HPijWKh5PK5tatW/ItYV8WEBBAnz59FFwiQRDqE9Fx10MdO3Z87T25BUFo/BrEw8n6yMbGRr6n9uuqrdh7XcXpBaEyXifyLuLuJYkRtyAICvU6kXcRdy9JqTvuM2fOsGXLFjQ0NOSj1DfeeIMjR44ARVH1H374gZiYGJ4+fYqGhgbr16+nS5cu8mukpaXh6urKunXr6NOnT4Wx+pc9e/aMjz/+mKtXr6KtrU1ISAg6OjocO3aMDRs2IJVK6dChA/7+/ujp6WFjY8P48eP5+eefefr0KWvXrqV37978+eefeHt7A0UHIRe7cuUKK1euJC8vj+zsbObOncuUKVOq1E4i8q44ylTXqmqIbaPUkffalJSURFxcHK1atWLIkCEsWbKEqKgoPD09iYuL49ixY4SHh9OkSRM2btzIzp07WbZsGQB3797F19eX1atX07dvX3bt2gWUH6t/WXZ2NjNnzsTU1JT58+dz6NAh/vOf/7B8+XJ27dqFkZER27Ztw9/fn6CgIABatWrF3r17CQ8PZ+vWrWzatIklS5awdOlShg4dSkhIiHwHwD179vDBBx8wePBg0tPTGT9+fJU7brGqRDGUpa6v25k1tLYR+3HXou7du9O+fXuaNm2Kjo4OgwcPBsDAwIBHjx6xfv164uLiWL9+PceOHSMvL0/+3QULFtChQwd5x3z69GmOHj2Kvb09b7/9Nnfv3iU1NbXc+7dt2xZTU1MAunXrRk5ODsnJyZiamspj7c7OzvIoPRRF7gH+9a9/8eDBA7Kzs7l37x5Dhw4FkMfroWgL3Pz8fLZu3cqGDRtKlF8QhIZJ6UfcGhoaJf69eAdAgL///htnZ2feffddrKys0NPTK5Fq9Pb2JiQkhOPHjzNixIgyY+/lKS0+X3wCfDGZTEZh4f/ivsU7B6qoqJT4Xml1+Oijj9DW1sba2pqxY8dy8ODB8htEEGrZ60TeRdy9JKUfcZfn4sWLvPnmm8yYMYM+ffpw5MgRJBKJ/H1TU1P8/Pzw9/cnLy+vzNh7VZmZmZGUlCRftRIREVFqyrOYjo4OBgYGHD9+HKBE53zq1Cnmz5/PyJEjOXnyJECJOgiCor1O5F3E3UtS+hF3eYYNG8bly5cZO3YsMpmMgQMHcvXq1RKfGThwIIMGDWLDhg14eHhUKlZfET09Pfz9/XF3d6egoAADAwNWrVpV7ncCAwPx9PRkw4YN9O3bV/76hx9+yNSpU9HS0sLY2BhDQ0MyMjJ48803q1wuQRDqBxF5FyokHk4qhrLUVdSzYiLyXsdEfF0QhJrWaOe4PT09sbW1rfLDuJpIRL6oOL4eHBxMr1695FvIxsTE1Hin7e3tzcWLF195vfhw5czMTObMmVOj9xQEQfEa7Yg7Ojqa5ORkNDU167ooANy5c4f09PRavUdF8+D6+vp89dVXtVoGQahIceS9Kp7lF4oHlC9olB33+++/j0wmY8iQIRQUFJCUlATApk2bgKIHdidPniQoKIjCwkKMjIxYuXIlOjo6ZV6ztAN9fXx8GDhwIE5OTgC4uLiwaNEizp8/T3R0NKqqqpiamuLv709AQAAZGRmsWLECX1/fUg8Qvn37Nm5ubnTp0oVr167Rs2dPzM3NiY6O5uHDh4SEhJS7F7mLiwvu7u5YWFiUerhyRkYG06ZNk5/4Iwh1QUTeq69RTpUUn/C+f/9+dHV1X3k/Ozub9evXs337dvbv38+wYcP47LPPyr3mhg0b2LFjB1FRURgaGnL9+nUmTpwo38Xv9u3bZGdn07t3b7Zu3cq+ffuIioqioKCAzMxMfHx86N27N76+vpw8eVJ+gPD+/fvJzMzkwIEDAKSmpjJnzhxiYmI4f/48t2/fJiIignHjxhEREVGp+r94uPLGjRu5detWVZpPEIR6rlGOuCuSlJTE33//zbRp0wCQSqW0bNmy3O+UdqCvTCZj2bJlZGRkEBMTg729PWpqapibmzNp0iRsbW2ZOXMm+vr63LhxQ36t06dPl3mAsJ6eHj179gSgXbt2JZKclZ17L+tw5dcl9ipRHGWqa1U1xLYRe5W8hpcThYWFhairqyORSOjXr598ZJ6fn8+TJ09KfNfb25tLly4BRas/yjrQd8KECcTFxREfH8/27dsB2Lx5MxcuXODkyZPMnj37ldG8RCIp8wDhl+fkX0xBvm69q3tAsFgOqBjKUlexV0nFlHo5oLa2tnwvj+bNm5OQkIC1tTVmZmb4+PiQlpZG586d2bx5M5mZmSXOenzxQV/xgb7h4eG4urpSUFBASkoK9vb2ODo6MnXqVLp164a+vj7Z2dm888477N27F3Nzc/l+JSYmJvLYuqWlJUFBQTg5OaGlpYWbmxsODg5YWFjUSL0HDx7M9u3bmTx5Mk+fPiUhIaFEKEcQ6pKIvFdfo+64W7RowezZs5k0aRLt2rWTL79r06YNq1ev5qOPPkIqlaKvr09gYGCZ1ynvQN/27dvTvn17HBwcgKJT152dnZk0aRJNmzalc+fOTJw4kfz8fHJzc/Hw8CAwMLDcA4Sr63UOVxYERSmOvAuvTyQnq0Emk3Hv3j1cXFw4ePBgvVl6WNPEVIliKEtdRT0rptRTJbXt8OHD+Pn54efnp7BOe+HChVy7du2V121sbFiwYIFCyiAIQt0SI26hQmLErRjKUldRz4qJgxQagTNnzuDi4lLXxRAEoZ4QUyWCIChUZSPvIuZeNtFxNxA5OTnMmjWLe/fuYWpqiq+vL6dOnSrzQGFTU1NSUlL4/vvvSUhI4Ntvv0UqldKrVy98fX3lp+gIgqJVNvIuYu5lE1MlDURGRgbLli3jwIEDPHnyhNDQUJYvX05ISAixsbH069cPf39/+eetrKw4fPgw2dnZREZGsnv3bmJiYtDV1ZUHhQRBaJjEiLuBGDBgAJ06dQLAzs6OpUuXYmFhUeJA4dDQUPnnzczMgKL58Zs3b8o3wiooKJBH6itLRN4VR5nqWhkNvT1E5F3JvRhbl8lk8oOCX3yttAOFJRIJY8aMwcfHB4AnT55U+cxJsapEMZSlrlXpzBpye4h13ALnzp3jzp07tGvXjv379zN79mzCwsLIyMjAyMiozAOFBw0axI4dO5g3bx6tW7fGz8+PMmJP3QAAIABJREFUjh07lnkqjyDUtspG3kXMvWyi424gunXrhpeXF//88w+WlpbMmjWLbt26VXigsLGxMe7u7kyfPh2pVIqJiQlz586tgxoIQhERea8+EcARKiSmShRDWeoq6lkxEcARBEFoZETHLQiC0MA0mo67vpzqXiw9PR0vL68av25VPX78GEdHR8aNG0dYWBgbN26s6yIJglBNjebhpDKe6l4ZKSkpaGpqEhUVVddFEQSgcpF3EXcvX6PouJX5VPeePXty7tw58vPzWbRoEWFhYfz111/MmDEDOzs7vLy8yMrK4v3332fUqFEkJiaWOOlHEBStMpF3EXcvX6OYKlHmU91lMhl79+5l9OjRBAQEEBwczM6dOwkJCUFXV5eAgAB69+4tbyNBEBq+RjHirkhjPtW9+AR3AwMDzMzMaNq0KYaGhjx69KjK7VQWEXlXHGWqa0UaQ1uIyHslKOOp7hoaGvI/V/c097KIddyKoSx1rWxn1tDbQqzjrqQXT3V//vw5CQkJQNGGSxcuXCAtLQ0o6mjXrVtX4rurVq0iJiaGmJgYTExMGDVqFDo6Ori6umJvb09KSgoAjo6O7N69m/bt28tPdR87dizdu3dnwYIFDB06lNTUVNTU1Eqc6h4TE8OTJ08oLCzEzc2Nw4cPK7BlBEFoTBrViFtZT3UXhIakMnuViH1Kyici71WgLKe6v0xMlSiGstRV1LNiYnfAGiROdRcEoT4QI26hQmLErRjKUldRz4op1cNJQRAEZSA67lpQW/ufvI5NmzbJE6SCUB8UR95f/qeFdtO6LlqDIea4BUFQqLIi7yLmXnmi4y7DmTNn2LJlCxoaGmRkZGBjY8Mbb7zBkSNHAAgNDeWHH34gJiaGp0+foqGhwfr16+nSpYv8Gmlpabi6urJu3Tr69OnDunXrSExMRCKR4OjoyIwZM8q8/+PHj/nkk0/IysoCwM3NDVtbWxITE/niiy949uwZjx49wtPTk5EjR3LlyhVWrlxJXl4e2dnZzJ07lylTpgCQnJzM22+/TV5eHk5OTkyfPr32Gk4QhFonOu5yJCUlERcXR6tWrRgyZAhLliwhKioKT09P4uLiOHbsGOHh4TRp0oSNGzeyc+dOli1bBsDdu3fx9fVl9erV9O3bl127dgFFuxg+f/6cWbNm0bt3bwYMGFDqvX/88UcMDQ0JDQ0lJSWFAwcOYGtry3fffUdAQABdu3bl9OnTrF69mpEjR7Jnzx4++OADBg8eTHp6OuPHj5d33P/88w/ff/89UqkUR0dHLCwsMDExqXQ7iMi74ihTXUvT2OovIu91oHv37rRv3x4AHR2dEvuIPPp/7J17XM/3+//vHR3mUMpxmVkfZIRsERJlDms6YsmnNsfNPjIbmpa0kLRoCGNs36EPOR9K5TBzaJNiQ1jC5vBulCUKpcO75++Pfr0+RQeUSM/77dbtpl6v5+F6tV0939frelxXZibBwcFERkZy5coVYmJiSjjDKVOmYGpqqjjm2NhYEhMTOXbsGABZWVkkJSWV6bjNzMz45ptvSE1NpX///kyaNAmABQsWcPDgQfbs2cPp06cV6b6XlxcxMTF89913XLhwgaysLGUuW1tb6tevDxTWYImPj38ixy2zSqqH2mJrec7sZbJfZpU8J4rXAYGSdURu3LiBi4sLd+/excrKCicnpxJ1UmbOnIlKpeLQoUNAYb0ST09PRVa/adMmhg8fXubar7/+OtHR0djZ2XHixAmGDx9OQUEBo0aNIiEhgc6dOzNx4kTl/s8++4z9+/djbGzMZ599VmKu4jVMCgoKnllNE4lEUj3I/4OfkjNnztCmTRtGjx7NgwcPCAkJoUWLFsr1Ll264Ofnx4wZM+jRowcWFhZs3rwZa2trcnNzGTVqFLNnz6Znz56lzv/f//4XlUrFl19+iZWVFdbW1mRmZnLlyhU2bNiArq4uCxcuRK1WA/Drr78SHR1N8+bNWb9+PYBybe/evbi5uZGdnc2hQ4dkiVfJc6UsybuUuT8+0nE/JZaWlpw/fx5bW1uEEJibm3Px4sUS95ibm9OzZ08WL16Mp6cnV69excnJifz8fJydnct02gCOjo5MnToVOzs7tLS08PT0RE9Pj+HDh/Pee++hra2NhYUFDx48ICsri8mTJzNq1Cjq1KmDiYkJr776qpKS2KpVK0aOHElOTg4ff/xxuc0ZJJJnja6O1ksVEnkeSOWkpEJkjLt6qC22SjsrRtYqeYG5du0akydPLvWav7+/Ut1QIpFIilOrHfeECRPw9/enefPmlZpHpVKxYsUKAgICnmjca6+9prRCqyxxcXEsW7aM0NDQKplPIpG8uNRqx7169eoqmedF6egukdQEind5l93cn44a67gf7pru6urK5MmTadeuHYmJiRgYGLBkyRL09PSIiooiJCSE+vXr07FjR9RqNYGBgdjY2LBu3Tri4+OJiYkhIyMDlUpFnz598PPzK3UdT09PNDQ0SuyleEf3e/fuldoJfuHChZiYmHDixAlycnLw9vbG0tKStLQ0fH19SUlJQUNDg2nTptG7d+8y7S4oKCAgIIDY2Fg0NDSwt7fno48+AgqbIk+YMIFr167Rtm1bQkJC0NXVZc2aNYSFhaGlpYW1tTWenp7P5pcikTwGxSXvUub+dNTIPO7SuqZHRERw/vx5xowZw+7du2nUqBERERGkp6cTEBDA2rVr2bp1KxkZGaXOefLkSUJCQggPD+fgwYMkJSWV2529OMU7upfWCb5r165AoYx9x44dBAcH4+XlRW5uLvPmzWPYsGFs376dFStW4Ovry71798q0PSwsjBs3bhAeHs6WLVvYt2+fkit+/fp1fH19iY6OJi0tjaNHj5KQkMCGDRvYunUr4eHhnDt3TumtKZFIaiY18sRdWtd0IQQGBgZKx/R27dqRkZHBiRMnMDMzU+LYjo6OSr2R4piZmdGgQeFb3NatW5ORkVFmd/by6Nmz5yOd4IsoOoV37NiRpk2bkpSUxNGjR/nrr78ICQkBChscq1SqMpWNcXFxODk5oaWlRb169bCzsyM2NhYbGxtMTExo3bo1AMbGxty+fZs///wTa2trGjYs/Gi6Zs2aih/wQ0jJe/VRm2wt4mW2WUrei1Fa1/SUlBROnTql3FPU8V1TU5OCgoIK56xTp84jY8vqzh4WFsbGjRsBGDlyZInCUhoaGqV2goeSyssiBWNBQQFr165FT08PgJs3b2JgYFDmPh+2pWifUFIhWWSDtrZ2idBOamoq9erVo1GjRhU+kyJkOmD1UFtsfdiZvaw2S8n7Q5TWNb2sj//du3fnzJkz3Lx5EyEEUVFRj8Son2SdvXv34urqqkjXXV1dS3R0h0c7wRcRFRUFFKouMzMzad++PRYWFmzYsAGAS5cuYWdnR3Z22S9rLCws2LlzJ2q1muzsbCIiIsoV8rz99tscPnxYsWHatGkyVCKR1HBq5Inbxsbmka7p5ubmpd7bpEkTfHx8GDt2LLq6uhgZGT32abO0dYq6uxfH2Ni4REf3hzvBF6FSqZSfLVq0CC0tLXx8fPD19cXOzg6AoKAgJWRTGi4uLly5cgUHBwfy8vKws7Nj4MCBxMXFlXp/p06dcHNzY+TIkRQUFDBw4MByX35KJM+a4pJ3KXN/Ol565eTt27cJDQ3Fw8MDTU1N/P39adOmDe7u7s9kvbI6wbu7u+Ph4VHu6fhFRYZKqofaYqu0s2JqvXJST0+PzMxMhg4dipaWFp06dVJeEj4LqqITfFRUFN99912p16pKsCORSGouL/2JW1J55Im7eqgttko7K+alfDkpkUgktZka77i//PJLBgwYwO7du59o3LPqxK5SqfD29q7yeStD0TMKDw9nwoQJz3s7EomkktT4GPeOHTtISEh46nhyVfMi1i0p/ozs7e2f93YkEkklqdEn7okTJyKEoHfv3oqsHGDp0qUsXboUKJTHDx8+HEdHRzw8PLh9+3a5c0ZERODg4ICzszOffvopOTk5eHp6snnzZuUed3d3Tp8+zY8//oi9vT2Ojo74+voChXVLzp49y+zZs4HCWidOTk7Y29sTFBSEEILk5GQcHBz4/PPPsbOzY8aMGWzcuBEXFxeGDBnCn3/+We4eS1t3+/btfPrpp/z73/9m0KBBzJ8/HyGE8oxGjBhBQkICNjY2T/6gJRLJi4Wo4bRv316oVCphbW2t/CwkJESEhISIW7duCXt7e3Hnzh0hhBBhYWHC29tbCCGEtbW1UKlUj8xnY2Mj0tLShBBCBAYGij/++EPExsaKUaNGCSGESE5OFra2tiI/P1/07NlT5ObmCrVaLby8vERKSoo4duyYcHNzE0IIcfjwYTF58mSRn58v1Gq1mDp1qti5c6dQqVSiQ4cO4ty5c0KtVot33nlHLFy4UAghxNKlS8W8efPKtLesdbdt2yZ69+4t/vnnH5GTkyNcXFzE3r17lWckhHjkOUkkkppJjQ+VlMfp06e5ceMGH3zwAVAoF2/cuHG5Y6ytrXF1deWdd95h8ODBdOzYESHEI/VHtLS0MDMzY/jw4QwYMIAxY8bQvHlzrly5osxVVq2Tt956C0NDQ6WuSosWLUp0kC8v9l7WugADBgzA0NAQKOzsfuzYMQYNGvR0D68YMqukeqgttko7K6ZW5HEX1eUoIj8/H21tbdRqNd27d1ea4+bk5HD//v0SY2fOnKlIwP39/fHx8eH8+fMcPnwYT09PPDw8cHBwKLX+yLfffsupU6c4cuQI48ePZ+HChSXmLqvWye3btx+JyRevY1IRZa37cC2UJ5lTIpHUHGp0jLuIRo0acefOHdLT08nNzSUmJgaArl27curUKS5fvgwUOrygoKASY+fNm6fUHenYsSODBg1CX1+fjz/+GAcHBxITE4FH64+kp6dja2tL+/btmTJlCn369CEpKalE3ZKyap1UhrLWBYiJieHu3bvk5OQQGRmJlZVVpdaSSCQvJi/Fibthw4aMHz+e4cOH06JFC6VXY9OmTQkICOCzzz6joKCA5s2bs2DBgjLn0dbW5tNPP2Xs2LHUqVMHAwMDAgMDAR6pP9KkSRNcXFwYPnw49erVo23btgwbNoycnJwSdUtKq3Xy999/P7WtZa27Z88emjRpwoQJE7h9+zb29vb07dv3qdeRSCQvLlI5+RiIMuqPvEhs376d+Ph45Q9NVSJj3NVDbbFV2lkxtSLG/aypivojT8q0adO4dOnSIz+3sbFhypQp1bIHiUTyYiJP3JIKkSfu6qG22CrtrJgaXavk8OHD9O3bl2nTplVqng4dOgCF/RrDwsIee1xcXNwzK/8KVSOPT05OlqIaiaSW8UKHSvbs2YOHhwcuLi5VMp+rq2uVzFNVvIjyeIlE8uJT4Yk7Li6OsWPH8p///IfBgwfz6aefcvny5RKnvOIS8z59+uDr64ujoyPjx48nOjqaUaNGYWNjQ3x8fKlrHDx4EAcHB+zs7PjPf/5DWloaW7Zs4cCBA6xYsYItW7Y8MiYiIgJbW1vee+89pWO6jY2NkvqXlZVFv379yMnJKXWflpaWzJ07F0dHR4YNG6Y40F9++YX33nsPZ2fnEjL3q1evMmbMGJycnHB1deWPP/4AwMvLi4kTJ/Luu+/y888/8/XXXyty9GXLlpX7bIvL4+3s7BSp+7Rp0/jqq6+Awu7zH330EVC6fL6I27dv06dPH/Ly8gC4cOGCUpdk586dODk54eDggLe3d4lnIpFIaiAVSSuPHTsmunXrJm7cuCHUarUYNmyYWLduXakScyEK5dX79+8XQgjh5uYmpk6dKoQQYvv27eI///nPI/OnpaUJS0tLRX6+evVqMXnyZCGEEDNmzBDbtm17ZExKSoro1auXuHHjhhBCiOnTp4v9+/eLJUuWiMWLFwshhNixY4fw9fVV9lTePufPny/mz58vcnJyRJ8+fcSlS5eEEEJ4e3sr8nUXFxdx7tw5IYQQFy9eFIMGDVL2OGPGDCHE/+TwQgiRlZUlpkyZIh48eFDusy2af8GCBSI0NFQIIcTQoUPF0KFDhRBCLFmyRKxfv75c+XzR72LixIni559/FkII8c0334jVq1eLCxcuCFdXV2UfCxcuFMuXLy9zTxKJ5MXnsUIl7dq1o0WLFkBhf8WMjIxy7y8Sfrz66qu89dZbQKGUOzMz85F7ExIS6NKlC0ZGRkBhT8VVq1aVO//Jkyfp3r27sqei3GwTExPGjBnDlClT2LFjB1OnTi13nqI853bt2nHixAmSkpJo1qwZxsbGADg5ObFkyRLu37/P2bNn+fLLL5WxWVlZSsGqLl26ANC8eXPq1KnDyJEjsba2Zvr06SW6x5dHv379WLNmDRYWFvzrX//ir7/+4tatWxw5coSQkBBCQ0PLlM8XYW9vT2RkJNbW1kRHRxMaGsr+/fu5evWq0vUnLy9Pkdo/LvLlZPVQW2yVdlZMlaQDFnc+RR3SRSkS8yKKp8w9LLtOTU1VPvo3a9aMkSNHlrguhCjRMb20MS4uLiU6taenpwNgZGREq1at2LdvH7du3SpRMbA8u4ok8w9L54v2XlBQgK6ubom2YSkpKejp6QFQt25doFDAs2XLFuLj4zly5AgjR44kNDSUtm3blrsPADMzM7y8vDh69Cg9evTAwMCAPXv2kJ+fT6tWrcqVzxcxYMAAAgMDOX78uKLwVKvVvPvuu/j4+ABw//591Gp1hfuRSCQvLk+VVdKwYcNSJeaPQ/PmzRWJ+erVq+natSunT59WCitt2rTpkYa6D48xNTXl1KlT/PPPPwAEBARw4MABAIYNG4a/v/9T1Z3u0KEDaWlpnD9/HoDIyEjF3tdff11x3L/++iv//ve/Hxn/xx9/4Obmhrm5OTNmzMDY2FiJuZdGcXm8trY2Xbp0ITQ0lB49emBhYcHKlSvp168f8HjyeV1dXfr27UtAQIBif8+ePdm/fz+3bt1CCIGfnx9r16594mcjkUheHJ4qq6QsifnTYGhoyJw5c/Dw8CAvL49WrVoxb968csc0b96cmTNnMm7cOAoKCujWrZsSQhg0aBCzZs3CwcHhifeio6PDN998g6enJ9ra2iVCCgsWLMDPz4/vv/8eHR0dFi1aVOLUD/Dmm2/SrVs3hg4dSr169ejevXu59UKMjY1LyOP79evH8ePHMTY2pmnTpty6dYv+/fsDhcKbx5HPOzg4EB4ezuDBg4HC8JGHhwcffvghBQUFdOzYUfn0IpFIaiYvlQBHCMGRI0cICwtTKgJKKo+McVcPtcVWaWfF1CrJe0BAAAcPHmT16tXPeysKUVFRfPfdd6VeKx4zl0gkksflpTpxS54N8sRdPdQWW6WdFVOjJe+lMWHCBFJTUys9z/bt2/Hy8qqCHf2P4vL4p4mxSyQSyeNQ40IlL1IY5GGKK0NlGEQikTwrqtVxr1q1iujoaNRqNZaWlri6ujJ58mTatWtHYmIiBgYGLFmyBD09PaKioggJCaF+/fp07NgRtVpNYGAgNjY2rFu3jvj4eGJiYsjIyEClUtGnTx/8/PxKXcfT0/ORDJDinDp1innz5pGTk4O+vj5z5syhTZs2JCYm4uvry4MHD2jcuDELFy7E0NAQPz8/Ll68SFpaGh06dOCbb75R2oeNGDGCLVu20KFDB5KSksjOzsbHx4ekpCQ0NDQYN24cjo6ObN++vcz9l0ZycjLjx49HX1+funXr8v333xMUFER8fDxqtRpnZ2dGjx5Nfn5+qftLS0srMf7HH3+syl+tRCKpTqpLolmaZHv58uVKt3MhhPDw8BDr1q0Tt27dEn369BEpKSlCrVaLSZMmKbLyou7s27ZtE/369RN3794VWVlZwsrKSpw/f75MafjDbNu2TcyYMUPk5OQIa2trcfr0aSGEEFFRUcLZ2VkIIYStra0iIV+/fr0IDAwU8fHxws/PTwghhFqtFm5ubmLPnj1CiP9J64v/++uvvxZz584VQghx69YtYWNjIxITE8vcf1moVCqlo70QQmzYsEEEBAQIIYTIyckRbm5u4vjx42Xu7+HxEomk5lJtJ+7SOp4LITAwMFDypdu1a0dGRgYnTpzAzMxM6V7u6OjITz/99MicZmZmNGhQGMBv3bo1GRkZZXZWL4srV67QqFEjRbb+7rvv4uvry99//80///yDtbU1AKNGjVLG6OnpsX79ev766y+uXLlCVlZWmfMfO3aMgIAAoLDt2IABA4iPj6dBgwal7r88DAwMlNIAsbGxJCYmcuzYMaBQgp+UlMS///3vMvdXfPyTIF9OVg+1xVZpZ8W8MOmApUm2U1JSOHXqlHJPkeRcU1OTgoKCCud8WIovhChTGh4WFsbGjRsBGDlypDK2tHXE/0+0KR5eycnJ4ebNm1y4cIGQkBA++OADnJ2duX37dgmZfFlzFf++SHJe2v7Lo0haD4XP09PTk0GDBgGFsv9XXnmFAwcOlLm/4uMlEknNpdqySkqTbJ89e7bUe7t3786ZM2e4efMmQgiioqLKjVFXtM7evXtxdXVVZPPF63K/8cYb3Llzh4SEBKAw77pVq1a8+uqrNG/enF9++QUofNm4ZMkSYmNjeffddxk2bBiNGjUiLi5OccTFJezF97N161ag0LkeOHCAHj16PNnDK8POzZs3k5eXx/379xk1ahSnTp0qd38SieTloNpO3KVJts3NzUu9t0mTJvj4+DB27Fh0dXUxMjKiUaNGT71OUWf20tDV1WXRokXMnTuX7OxsGjduzKJFi4D/ydwXLFiAvr4+QUFB3L59m+nTpxMZGYmOjg7du3dX6qwMGDAABwcHtm/frsw/adIk/Pz8sLOzQ61WM3HiRDp16kRSUtLjPrpSGTlyJFevXsXJyYn8/HycnZ3p2bMnenp6Ze5PIpG8HLyQApzbt28TGhqKh4cHmpqa+Pv706ZNm2faRkxSNjLGXT3UFlulnRXzwsS4nwQ9PT0yMzMZOnQoWlpadOrUSakn/TJz7do1Jk+eXOo1f3//ShXzkkgkLw8v5Ilb8mIhT9zVQ22xVdpZMS+d5L06sLGxkXFhiUTywiIdt0QikdQwXsgYd1UQFxfHypUr0dHRITk5GRsbG+rXr68IeVatWsWePXvYtWsX2dnZ6OjoEBwczBtvvKHMcfnyZT7++GOCgoIwNTUtVWJeFsnJyUyaNIk33niDS5cu8eabb2JmZsaOHTvIyMhg+fLlGBsbk5CQwPz583nw4AH6+vrMnj2b1q1b4+7uTuPGjbl48SKLFy/m0qVLrFixAg0NDUxNTZk7d65Sc7woLl68HMCOHTu4c+cOvXv3ZufOnRw4cIAGDRqQnJzMRx99RFRU1LN7+BKJ5NnyPOSa1cGxY8eEmZmZuH79usjKyhLdunUTYWFhQgghvLy8xJo1a8SHH34osrOzhRBCLF68WMyZM0cIUSirP378uLC1tRXHjx8XQpQtMS8LlUqlyPnVarV45513xMKFC4UQQixdulTMmzdP5OTkCDs7O/H3338LIYQ4cuSI+PDDD4UQQri5uSkd6cvqal+8a33RvovKAQwcOFDk5eUJIYT44osvxJYtW5S1v/vuu0o+XYlE8jx5aU/cAO3bt6dly5YA6Ovr06tXL+B/HeeDg4OJjIzkypUrxMTE0LFjR2XslClTMDU15e233wbKlpgXXS8NQ0NDRc7fokWLEusnJydz5coVVCoVn3zyiTLm3r17yr+LZPhldbVPTEwsc+0333xTaeA8bNgwli5dyvDhw9m9e/cT95yULyerh9piq7SzYmpkOmBVoaOjU+L74h3nb9y4gYuLC25ublhZWWFoaFjCEc6cOZPly5dz6NAh+vfvX6bEvDyKd7t/eH0olNsbGRkpJWDVajVpaWnK9eLd40vraq+hoVFCsp+Xl/fIWABzc3Nu3rzJvn37MDIyUmrASCSSmkmtfTl55swZ2rRpw+jRozE1NeWnn34qIQ3v0qULfn5+zJkzh6ysrDIl5pXhjTfeUIpqAWzbto3p06c/cl9ZXe319fW5dOkSAAkJCcr1h9HQ0MDR0RF/f3+l+JZEIqm5vNQn7vKwtLTk/Pnz2NraIoTA3NycixcvlrjH3Nycnj17snjxYjw9PUuVmFcGXV1dlixZotQCb9CgAV9//fUj95XV1T4zM5O9e/dia2tLp06dSnSlf5j33nuP//u//+Odd96p1J4lEsnzRwpwagEFBQWEhYVx+fJlfHx8nni8jHFXD7XFVmlnxdTqGPezpqZI1D08PLhx4wY//PDD896KRCKpAuSJW1Ih8sRdPdQWW6WdFVPrJO/PQ66+dOlSli5dCkCHDh2qdW2JRFL7kKESiURS5TRsVI+6dUp3L7l5srFHZXkhHffzlqvPnz+f5s2bM3bsWKBQUm5vb4+ZmRkzZ87k+vXraGtr8/nnn2NlZVXqHL///jteXl6sXr2a69evK6KZxo0bExwcjLe3N66urvTr149vvvmGP/74g++//56bN28yduxYdu/ezaJFi4iNjSUjI4NmzZqxaNEiDA0NiYqKIiQkhPr169OxY0fUajWBgYHlyudNTU357bffSE9Px8fHh379+lXRb0sieZS6dbSxm7ar1GsRwQ7VvJuXkOeq2yyD5y1XP3funHBychJCCHH37l3Rp08fkZOTIz799FPxf//3f0IIIa5duyb69Okj/vnnnxLS8/bt24vExEQxZMgQ8eeffwohCuXrRV3kV61aJWJiYsSGDRtEYGCgEEIIV1dXYW1tLfLz88XWrVtFUFCQuHLlivDw8BBqtVoIIYSnp6f44YcfxK1bt0SfPn1ESkqKUKvVYtKkSUq3+vLk8/7+/kIIIQ4cOKDYJpE8S4ZO3Vnql6TyvJAnbni+cvU333yT3Nxcrl69ysmTJ7GxsUFXV5djx47h7+8PFHZl79q1K6dPn35k/Lhx4xgyZIjyCWDAgAF4eHjwzjvvMGDAAPr06cONGzemUPFqAAAgAElEQVT45JNPFIl7hw4dOHfuHEeOHMHd3Z02bdowY8YMtmzZwuXLlzl16hSvvfYaJ06cwMzMTFE/Ojo68tNPP1Uon+/bty8A7dq1486dO0/0u5AvJ6uHl8nWpk0blnv9ZbGzPGplOuDzlqvb29sTFRXFyZMn+eijj4DyO7YXZ+HChXzxxReMGDECExMTRo8ejbW1NQcPHmTBggUkJCTwySefUFBQwL59++jevTuGhoYcO3aMc+fOYWZmxtmzZ5k2bRqjR49m8ODBaGpqIoRAU1Oz1M70FcnnizrKP27TZYlE8uJSI7NKqkOubmdnR1RUFFevXuWtt94CSnZsV6lU/P7773Tr1u2Rsb169WLatGn4+PhQUFDAiBEjuH//PqNHj2b06NH88ccfAFhZWbFixQp69OiBhYUFoaGhdO3aFS0tLY4fP06PHj1wdXXl9ddf59ChQ6jVarp3786ZM2e4efMmQgiioqLQ0NB4bPm8RFIdPMjJJyLYodQv+XKy8rywJ+7yqA65esuWLdHX18fMzEw5pc6cORNfX1+li7u/vz/NmjUrdbyjoyPbt28nNDSUqVOn4uXlhba2NvXr11fCLf379+fHH3/krbfeon79+uTl5WFtbQ2Ara0tHh4e2NnZAdC5c2eSk5Np0qQJPj4+jB07Fl1dXYyMjGjUqNFjy+clkurgbmY2ZQUJKgqjSCpGCnBqGLdv3yY0NBQPDw80NTXx9/enTZs2uLu7P7M1ZYy7eqgttko7K6bGxrifNTVFrv4wenp6ZGZmMnToULS0tOjUqRPvv//+896WRCKpRuSJW1Ih8sRdPdQWW6WdFVPrJO9lMWHCBFJTUys9j0qlwtvbuwp29PTExcU909CIRCJ5sak1oZLVq1dXyTzXr19HpVJVyVwSycuKlLw/W2qE4161ahXR0dGo1WosLS1xdXVl8uTJtGvXjsTERAwMDFiyZAl6enplysGLd0CPiYkhIyMDlUpFnz598PPzK3UdT0/PR/Ke/f39SU5OZvbs2dy7dw9zc3Mlxuzu7s706dNZuHAhJiYmnDhxgpycHLy9vbG0tCQtLQ1fX19SUlLQ0NBg2rRp9O7du0y7CwoKCAgIIDY2Fg0NDezt7ZWc8vT0dCZMmMC1a9do27YtISEh6OrqsmbNGsLCwtDS0sLa2prx48czdOhQDh06hI6ODhcuXGD69OmEh4c/m1+WRIKUvD9rXvhQyZEjRzh79ixbt25l586dpKamEhERwfnz5xkzZgy7d++mUaNGREREkJ6eTkBAAGvXrmXr1q1kZGSUOufJkycJCQkhPDycgwcPkpSUVOo6pTk3Hx8fOnfuzFdffcWwYcMUwcvff/9Neno6Xbt2BQpVizt27CA4OBgvLy9yc3OZN28ew4YNY/v27axYsQJfX98S6saHCQsL48aNG4SHh7Nlyxb27dvHoUOHgMKTv6+vL9HR0aSlpXH06FESEhLYsGEDW7duJTw8nHPnzvH333/TpUsXfvnlFwAiIyOxt7evzK9EIpE8Z174E3dsbCwJCQlKr8QHDx4ghMDAwEBp1dWuXTtFfFKaHPxhzMzMaNCgMPDfunVrMjIySl2nVatW5e6tZ8+ezJo1i+TkZHbt2oWDw/9OEkWn8I4dO9K0aVOSkpI4evQof/31FyEhIQDk5+ejUqlKyPWLExcXh5OTE1paWtSrVw87OztiY2OxsbHBxMSE1q1bA2BsbMzt27f5888/sba2pmHDwjzZNWvWAIUq0MjISKytrYmOjiY0NLSCp16S8l6SVAe1Ke+3ttgq7awcL7zjVqvVfPjhh4wZMwaAzMxMUlJSSigfNTQ0ypWDP0yR/Lv42NLW0dLSIiwsjI0bNwIwcuTIEhUIi5rwRkZGEh0dXaLDTHGJfkFBAdra2hQUFLB27Vr09PQAuHnzJgYGBmXu82FbikvstbX/96srsuHhbvCpqanUq1ePAQMGEBgYyPHjx2nZsuUTd3mXWSXVw8tkq6xVUsuzSiwsLNi1axf3798nPz+fSZMmcfbs2VLvLUsO/rTr7N27F1dXV3bt2sWuXbtwdXVFS0uL/Px8ZZyzszMbN258xCFGRUUBhfL8zMxM2rdvj4WFBRs2bADg0qVL2NnZkZ2dXe6edu7ciVqtJjs7m4iIiHIVn2+//TaHDx9WbJg2bRpnz55FV1eXvn37EhAQIMMkkmpBSt6fLS/8idvGxobz58/z/vvvo1ar6du3L+bm5qXeW5Yc/GnXcXJyeuQ+Y2Nj7t69i6enJwsWLKBly5a0bNnykXtVKpXys0WLFqGlpYWPjw++vr6KjD0oKEgJ2ZSGi4sLV65cwcHBgby8POzs7Bg4cCBxcXGl3t+pUyfc3NwYOXIkBQUFDBw4UHn56eDgQHh4OIMHD36s5yGRVAYpeX+2vFQCnOqWgwshuHnzJu7u7uzevRtdXV2gMLvEw8OjwnooNQUZKqkeaout0s6KqVWS9+qWg+/duxc/Pz/8/PwUp/2kREVF8d1335V6rShjRSKRSIrzUp24Jc8GeeKuHmqLrdLOiqnxLyclEolEUhLpuJ8QGxsbkpOTn/c2KsTLy0upGy6RVBcNG9WjadOG5X7JrJLK81LFuCUSyfOlPKl7EVLyXnlqpeOOi4tj5cqV6OjokJycjI2NDfXr11dUlqtWrWLPnj3s2rWL7OxsdHR0CA4OLiG+uXz5Mh9//DFBQUGYmpoSFBREfHw8arUaZ2dnRo8eXeb6ycnJTJo0iTfeeINLly7x5ptvYmZmxo4dO8jIyGD58uUYGxsTHR3Njz/+yIMHD8jNzSUgIIDOnTszYsSIEnM5ODgwa9YsAgMDOXToEM2aNUOtVtOjRw+gMB0xNjaWjIwMmjVrxqJFizA0NHw2D1cikTxzaqXjBjh9+jSRkZHo6enRu3dvZsyYwfbt2/nyyy+JjIzk4MGDhIaGUrduXZYsWcL69euZNWsWACkpKXz11VcEBATQrVs3wsLCANixYwe5ubmMGzeOzp07l9lFHiApKYn58+djYmLC4MGDadasGZs2bWLZsmVs2rQJLy8vNm7cyMqVK2nSpAlbt25l1apVrFy5Usk2OX36NF988QUeHh7s3buXP/74g927d3P37l1FaHP16lX++usvNm7ciKamJl988QXh4eGMHTv2sZ+VlLxXH7XFVmln5ai1jrt9+/a0bNkSAH19fXr16gVAq1atyMzMJDg4mMjISK5cuUJMTEyJeiJTpkzB1NRUccyxsbEkJiZy7NgxALKyskhKSirXcRsaGiq1Vlq0aFFi/eTkZDQ1NVm+fDk///wzly9fJj4+Hk3N/72SSE1NZdq0aYSEhNCkSRPi4+MZNGgQOjo6NGnSBCsrKwDatGnDjBkz2LJlC5cvX+bUqVO89tprT/SsZFZJ9fAy2Pq4jqqm2/k4yDzuZ4COjk6J74vXFrlx4wYuLi64ublhZWWFoaEhiYmJyvWZM2eyfPlyDh06RP/+/VGr1Xh6ejJo0CCgsOTqK6+8Uu76D+d9F18f4P79+wwfPhx7e3vMzc3p0KED69evByAnJ4f//Oc/TJ48WXH+RfVKiiiqZXL27FmmTZvG6NGjGTx4MJqamsgMUMmzokjqXh7y5WTlkVklpXDmzBnatGnD6NGjMTU15aefflKKOwF06dIFPz8/5syZQ1ZWFhYWFmzevJm8vDzu37/PqFGjShTBehquXLmChoYGEydOpGfPnuzfv1/Zg7e3N+bm5iWqEfbq1Yvo6Ghyc3PJyMggJiYGgOPHj9OjRw9cXV15/fXXOXToUAlbJJKq5G5mNv/8c7fcL10drYonkpRLrT1xl4elpSXnz5/H1tYWIQTm5uZcvHixxD3m5ub07NmTxYsX4+npydWrV3FyciI/Px9nZ+dKy91NTEzo2LEj7777LhoaGlhaWvLbb7/x+++/s3v3bjp37oyjoyNCCP71r38RHBzMmTNnGDp0KIaGhhgbGwNga2uLh4eHUh+lc+fONSKdUSKRlI1UTkoqRMa4q4faYqu0s2JkjPs5ce3aNSZPnlzqNX9/f0xNTat5RxKJ5GVBOu5nxGuvvSaLREkkkmeCdNxVSFFDYiMjo2pbc+nSpQBlnu4lkuqkvO7uRcisksojHbdEIqkypOS9epCO+yGetxx+/vz5NG/eXFE2Tp48GXt7e8zMzJg5cybXr19HW1ubzz//XBHZAKxbt46rV68q6s7AwEBatGiBi4sLPj4+JCUloaGhwbhx43B0dHwGT04ikVQX0nGXwvOUwzs4OCjt1+7du8fJkycJDg7G09MTCwsLxowZg0qlwtXVlZ07dyrjhg4diqOjI97e3mhqarJv3z42bdrE0qVL0dfXZ/fu3aSnpzNixAhMTEwwMTF57OchJe/VR22xVdpZOaTjLoXnKYd/8803yc3N5erVq5w8eRIbGxt0dXU5duwY/v7+ALRu3ZquXbty+vRpZVyTJk0wMTEhLi4OHR0d2rZtS9OmTTl27BgBAQHKPQMGDCA+Pv6JHLdMB6weXgZbpeT9f8h0wGrmecvh7e3tiYqK4uTJk3z00UcAj8jUhRCPKCAdHByIiopCR0dHEdw8zjiJpKqQkvfqQUren5DqkMPb2dkRFRXF1atXeeuttwCwsLBg69atQGEH+d9//51u3bqVGDdgwACOHz/Or7/+ysCBAx8Zl56ezoEDB5RyrxJJVSMl79WDPHE/IdUhh2/ZsiX6+vqYmZmhoaEBFJ7kfX19la42/v7+NGvWrMS4unXr0r17d3Jzc5VT/aRJk/Dz88POzg61Ws3EiRPp1KlTVT0OiUTyHJCSd0mFyBh39VBbbJV2VoyMcb+ASDm8RCKpDLXOcU+YMAF/f3+aN29eqXlUKhUrVqxQMjaehKqSw8fFxbFs2TJCQ0Mf6/7r168zZswY6taty/r162nQ4Pmm+Ukkkqej1jnu1atXV8k8169fR6VSVclc1UV8fDydO3cmODj4eW9F8pLxOFL3ImRWSeWpUY571apVREdHo1arsbS0xNXVlcmTJ9OuXTsSExMxMDBgyZIl6OnpERUVRUhICPXr16djx46o1WoCAwOVeiLx8fHExMSQkZGBSqWiT58++Pn5lbqOp6en8pKwCH9/f5KTk5k9ezb37t3D3Nyc999/HwB3d3emT5/OwoULMTEx4cSJE+Tk5ODt7Y2lpSVpaWn4+vqSkpKChoYG06ZNo3fv3mXaXVBQQEBAALGxsWhoaGBvb6+kCaanpzNhwgSuXbtG27ZtCQkJQVdXlzVr1hAWFoaWlhbW1tYMHTqUxYsXk5WVha+vL3PmzHk2vyRJreRxpO5FSMl75akx6YBHjhzh7NmzbN26lZ07d5KamkpERATnz59nzJgx7N69m0aNGhEREUF6ejoBAQGsXbuWrVu3kpGRUeqcJ0+eJCQkhPDwcA4ePEhSUlKp64SHhz8y1sfHh86dO/PVV18xbNgwJfTx999/k56eTteuXQG4d+8eO3bsIDg4GC8vL3Jzc5k3bx7Dhg1j+/btrFixAl9fX+7du1em7WFhYdy4cYPw8HC2bNnCvn37OHToEFB48vf19SU6Opq0tDSOHj1KQkICGzZsYOvWrYSHh3Pu3DnUajWffvopNjY20mlLJDWcGnPijo2NJSEhAWdnZwAePHiAEAIDAwOl72K7du3IyMjgxIkTmJmZKXFsR0dHpdZIcczMzJQ4b+vWrcnIyCh1nVatWpW7t549ezJr1iySk5PZtWtXiZZiRafwjh070rRpU5KSkjh69Ch//fUXISEhAOTn56NSqUooMIsTFxeHk5MTWlpa1KtXDzs7O2JjY7GxscHExITWrVsDYGxszO3bt/nzzz+xtramYcNCFduaNWsAuHDhQgVPuXSk5L36qC22SjsrR41x3Gq1mg8//JAxY8YAkJmZSUpKSgkxS1HDXE1NTQoKCiqcs06dOo+MLW0dLS0twsLC2LhxIwAjR44sUVRKQ0MDR0dHIiMjiY6O5ocfflCuFVddFhQUoK2tTUFBAWvXrkVPTw+AmzdvYmBgUOY+H7aluPqxqClwcRu0tbVLhHZSU1OpV69ehc+jLGQ6YPVQk219UgdVU+18Ep5lOmCNCZVYWFiwa9cu7t+/T35+PpMmTeLs2bOl3tu9e3fOnDnDzZs3EUIQFRX1SIz6SdbZu3cvrq6u7Nq1i127duHq6oqWlhb5+fnKOGdnZzZu3EjLli1LZKxERUUBhYrLzMxM2rdvj4WFBRs2bADg0qVL2NnZkZ2dXe6edu7ciVqtJjs7m4iIiHJFPG+//TaHDx9WbJg2bVqZz0oikdQ8asyJ28bGhvPnz/P++++jVqvp27cv5ubmpd7bpEkTpcKerq4uRkZGNGrU6KnXcXJyeuQ+Y2Nj7t69i6enJwsWLKBly5a0bNnykXtVKpXys0WLFqGlpYWPjw++vr5KPZGgoKByU/NcXFy4cuUKDg4O5OXlYWdnx8CBA4mLiyv1/k6dOuHm5sbIkSMpKChg4MCB9O7dW1FdSiRVzePUKClCZpVUnpdSOXn79m1CQ0Px8PBAU1MTf39/2rRpg7u7+zNZTwjBzZs3cXd3Z/fu3ejq6gKF2SUeHh6V7vj+vJGhkuqhttgq7ayYWqmc1NPTIzMzk6FDh6KlpUWnTp2Ul4TPgr179+Ln54efn5/itJ+UqKgovvvuu1Kvyd6VEomkOC/liVtStcgTd/VQW2yVdlbMS/NyUiKRSCSFvJShkmfF8+ji7uXlRY8ePZS8conkRURK3qsX6bglEkmlkZL36qXWOe7n3cX93r17TJ06lbS0NKCw0cGAAQNKZKAkJyfzwQcf8PPPPwNw6NAh/vvf/5KXl8cnn3yCra0t9+7dw9vbm9TUVG7evEmvXr2YN28e8fHxLFiwgIKCAtq1a6d8OigqI/s8PjVIJJKqpdY5bni+Xdz379/Pq6++yqpVq0hMTCQ8PJwBAwaUu9/s7Gw2b97MrVu3GDZsGObm5sTFxdGxY0dCQkLIzc3lvffe49y5cwBcuXKFgwcP0rBhQ5YuXVrp5yUl79VHbbFV2lk5aqXjfp5d3M3MzPjmm29ITU2lf//+TJo0qcL9Ojk5oa2tTfPmzenWrRunT59m6NChJCQksGbNGv766y/u3LlDVlYWAG3btlXqlFQFMqukeqjJtkrJ+6PIrJIq5nG6uN+9excrKyucnJxKdEqfOXMmKpVKqc5X1MW9SA6/adMmhg8fXubar7/+OtHR0djZ2XHixAmGDx+u1CIpWqe4lP7h/RUUFKCjo0NoaChBQUE0adIENzc3jI2NlfF169ZV7i+qX1JEXl7eYz0jiUTy4lIrT9zlUbyL+4MHDwgJCaFFixbK9aIu7jNmzKBHjx5KF3dra2tyc3MZNWoUs2fPLlMt+d///heVSsWXX36JlZUV1tbW3Lt3D319fS5duoSFhcUjlQwjIyMZNGgQ169f5+zZs/j7+xMWFoaLiwt2dnacOXOG8+fPU1BQgKZmyb/F+vr6ijQ+ISGBf/75p4qfmEQiJe/VjXTcD/Gsu7g7OjoydepU7Ozs0NLSwtPTk0aNGjF+/Hi8vLzYtm3bIzHv+vXr4+zsTH5+PnPmzKFJkyZ8+OGH+Pn5sWrVKho0aICZmRnJycm89tprJcba2tqyd+9ebG1t6dSpk1ICVyKpSu5mZvO4QYHaEt9+lkjlpKRCZIy7eqgttko7K6ZW1ip53sgu7hKJ5FkiHfcT8Lg50FXVxb2qSUhIYO/evXh6ej7vrUgkkkogHXct4tKlS9y6det5b0PykvEkcneQLyerglrnuJ+3cjI5OZlPPvmEN954g0uXLtGqVSsWLFiAnp4eR44cISQkhPz8fIyMjJg7dy76+volTvpxcXEsW7aM0NBQLly4gJeXF2q1mrfffpsjR46wf/9+0tLSmDlzJtevX0dbW5vPP/+cbt26ERISQlZWFitWrOCTTz551o9aUkt4Erk7SMl7VVAr87hPnz7N7Nmz2bZtG+vXr6dJkyZs376dDh06EBkZyU8//URoaCi7d++mf//+rF+/XhmbkpKCh4eHopzcvHkzUKic3Lp1KwcOHODEiRPlrn/hwgVGjRpFZGQkxsbGLFu2jPT0dIKDg/nhhx/YuXMnlpaWLFy4sNx5vLy8mDJlCrt27aJ169ZKH8q5c+diYWFBREQEISEheHt7k5ubq3R5l05bIqnZ1LoTNzxf5SQUinCKUgYdHR2ZPn06ffr04caNG3zwwQdAodCmcePGZc5x584d/v77b/r16wfAsGHDWLduHQDHjh3D398fKOxe37VrV06fPv3kD+r/IyXv1UdtsVXaWTlqpeN+HOWkm5sbVlZWGBoakpiYqFyfOXMmy5cv59ChQ/Tv319RTg4aNAiA9PR0XnnllXLXL96ZXQiBlpYWarWa7t27s3LlSgBycnK4f/9+ifvgf6pKLS0tysrkfPjnxbvCPw0yHbB6qKm2Po1zqol2PilS8l6NFFdOmpqa8tNPP5VwekXKyTlz5pCVlaUoJ/Py8rh//z6jRo3i1KlT5a5x+fJl5Y/Btm3bsLKyomvXrpw6dYrLly8D8O233xIUFASgqCoBDhw4AEDDhg1p3bo1hw8fBiAiIkKZ38LCgq1btwKFzYp///13unXr9khneolEUjOplSfu8njWykmAxo0bExISwrVr1+jQoQP+/v7Ur1+fgIAAPvvsMwoKCmjevDkLFiwA4NNPP2Xu3LksW7YMS0tLZZ6goCC8vb1ZvHgxHTp0UGqUzJw5E19fX6Wru7+/P82aNaNLly4sW7aMhQsXMn369Kp8bJJazJPI3UFmlVQFUjlZzTxca7syLFu2jPfff59mzZqxb98+IiIiqqSM68PIUEn1UFtslXZWjFROPgcqUk5WFa1atWLs2LFoa2vTqFEj5s2bV2VzSySSFxd54pZUiDxxVw+1xVZpZ8XIl5MSiUTyklHrHLeNjQ3JycnVstaECRNITU1FpVLh7e39VHO4u7sr9bSLExYWprRNKw0vLy/l5eSTjJNInpSGjerRtGnDx/6SLycrj4xxP0NWr14NFMrsVSpVlc7t6upareMkkrKQkvfqp0aeuOPi4hgzZgwfffQRtra2LFy4kG+//RZnZ2ecnZ1JS0vjv//9LyNGjGDo0KE4OTnx119/lZjj8uXLDBo0iFOnTqFWq5k/fz5OTk7Y29uzZs2actdPTk7GwcGBzz//HDs7O2bMmMHGjRtxcXFhyJAh/Pnnn8D/Tvf+/v6cPXuW2bNnk5KSgpubG87OzgwfPlzJ+S7+SSAuLg53d3dlvc2bN+Po6Iijo6Ny+l66dClLly4lLy8PT09P5XqRBB8Ku8MPHz4ca2trNm3aVGKcRCKpudTYE/fz7NQOkJSUxPz58zExMWHw4ME0a9aMTZs2sWzZMjZt2lQiNOLj48OyZcv46quvWLZsGf3792f8+PEcOXKE3377jW7dupVra/369dm5cyfnz5/no48+KtHa7OTJk2RkZLBz505SU1MJDg7m/fffByA3N5ctW7Zw8eJFPvjgA1xcXJ7qWUvJe/VRW2yVdlaOGuu4n3e9EUNDQ6UNWIsWLUqsX14MvVevXkyePJnExET69euHm5tbhbYWNR82MTHBwMCgxKeHdu3acfnyZcaNG4eVlRVffPGFcm3AgAFoaGjQrl07bt++XeE6ZSGzSqqHmmqrlLyXjswqKYXn2akdQFdXt8z1y+Ott94iMjISS0tLoqKimDhxonLtcbu8F691oq+vT2RkJG5ubly+fBknJycyMzNLjNPQ0HisvUkkkppBjT1xl8ez7tT+pBSvERIUFETz5s358MMP6dmzJ05OTsD/6pG0bt1aqUdSREREBJ07d+bMmTPcv3+fNm3aKNcOHDhAeHg4ixcvpm/fvsTGxnLjxo0q2bdE8jhIyXv181I67uqoN/IkGBsbc/fuXTw9PZk6dSrTpk1j+/btaGlp8fXXXwNl1yOBwtCNo6MjmpqaBAcHl/i0YWVlxb59+3jvvfeoU6cO9vb2dOjQocr2LpFUxJN0eIfaE99+lkjlpKRCZIy7eqgttko7K0bWKnlKZKd2iUTyoiIddxm8qJ3aJRKJpMZmlVSGL7/8kgEDBrB79+4nGleWXP7gwYP8+OOPVbU9iaTG8KRydyl5rxpq5Yl7x44dJCQkPJLS97ScPXu2SuaRSGoaTyp3Byl5rwpqneOeOHEiQgh69+5NXl6e0kS3SAY+efJkjhw5QkhICPn5+RgZGTF37lz09fVLne/SpUts3LgRKBTirFixgh9++IG2bduSlZXFu+++y759++jfvz8DBw7k5MmTvPLKKyxcuBAjIyMSEhKYP38+Dx48QF9fn9mzZ9O6desy95+SksL06dPJyMigffv2HD9+nCNHjpCamoq3tzd3797l5s2bODk5MWXKFM6fP4+vry/5+fnUqVOH+fPn8/rrr1ftQ5VIJNVKrXPcK1eupEOHDuzcuVPpqF6c9PR0goODWbduHY0bN2bjxo0sXLiwzCYF//rXvxg5ciQAI0aM4MaNG4SHhzNlyhTFYdepU4f09HTMzMyYM2cOoaGh+Pv7ExISgo+PDytXrqRVq1bExMQwa9ascmulzJs3j3fffZd///vf7N+/Xwn37N69W6nLcvfuXfr164e7uztr165lzJgxvPvuu+zYsYNTp049seOWkvfqo7bYKu2sHLXOcVfE6dOnuXHjhuLUCwoKaNy48WOPd3Z2ZsyYMUyZMoUdO3YwdepUAOrUqYOjoyMATk5OfPPNN1y5cgWVSsUnn3yijL9371658//666/Mnz8fgIEDB9KoUSMAxo0bx7Fjx/jhhx+4ePEieXl5ZGdn069fP+bMmUNMTAw2NjZYW1s//sP4/8h0wOqhJtr6tI6pptn5NMh0wGeAhoZGCRl8fn4+2traqNVqunfvzsqVKwHIycnh/v37Jf5NwcUAACAASURBVMbOnDlTiWs/3IrMyMiIVq1asW/fPm7dukXXrl0B0NTUVKTnBQUFaGlpUVBQgJGRkZK9olarSUtLK3ffWlpalJZ6HxgYiEqlYujQobzzzjscPXoUIQRDhgzBzMyMgwcPsmbNGg4dOlSl7dMkEkn1U2sdd6NGjbhz5w7p6ek0aNCAmJgYrK2t6dq1Kz4+Ply+fJm2bdvy7bffkpqaSmBgoDL24bBJTEwMOTk5yvfDhg3D39+/RAGp7Oxsfv75Z2xsbNi+fTtWVla88cYbZGRkcOLECd5++222bdtGREQEoaGhZe67V69eREREMGrUKA4fPqzUJfn111+ZPXs23bt359ChQ6SmplJQUMBnn33G0KFDGTlyJMbGxsppXSKpCp5U7g5S8l4V1FrH3bBhQ8aPH8/w4cNp0aKFIqhp2rQpAQEBfPbZZxQUFNC8eXMWLFhQ7lzm5ubMmDEDQ0ND3N3dGTRoELNmzcLBoeR/0Hv27GHRokU0a9aMr7/+Gl1dXZYsWcK8efPIycmhQYMGigS+LGbOnMmMGTPYvHkzJiYmSqjk448/5osvvqBu3bq0aNGCzp07k5yczMSJE5k5cybLly9HR0cHPz+/p39oEslDPKncHWpPfPtZIiXvVYwQgiNHjhAWFqaEWwA6dOhAUlJSpedft24dvXv35l//+hfnzp1j1qxZpbYoq0pkjLt6qC22SjsrRsa4q5mAgAAOHjyotC17Gr7++muOHj36yM87d+7MoEGDmDp1KpqamtSpU4e5c+dWZrsSiaQGIk/ckgqRJ+7qobbYKu2sGHnilkgkz4yGjepRt86TuRH5crLySMddhdjY2LBu3TqMjIyeeo7U1FR8fHwqFWqRSKoLKXl/PtTKIlMvMs2bN5dOWyKRlIs8cT9EXFwcK1euREdHh+TkZGxsbKhfv77SWX3VqlXs2bOHXbt2kZ2djY6ODsHBwbzxxhvKHJcvX+bjjz8mKCgIU1NTgoKCiI+PR61W4+zszOjRo8tcPzk5mQ8++ICff/6ZiIgIvv/+e7S0tDAyMmLBggWcOnWKZcuWKbneXl5e9OjRg/r167NixQqgUOBz4cIFtmzZwoYNG+jRowfOzs7A02W3SMl79VFbbJV2Vg7puEvh9OnTREZGoqenR+/evZkxYwbbt2/nyy+/JDIykoMHDxIaGkrdunVZsmQJ69evZ9asWUBhEaivvvqKgIAAunXrRlhYGFBYkTA3N5dx48bRuXPncjvIF7F48WI2b96MgYEBX3/9dYnu7g8zZMgQhgwZAhSqOd9++226dOnChg0bKv085MvJ6qEm2iol72UjX05WM+3bt6dly5ZAYRPfXr16AdCqVSsyMzMJDg4mMjKSK1euEBMTQ8eOHZWxU6ZMwdTUVHHMsbGxJCYmcuzYMaCwf2RSUtJjOW5ra2tcXV155513GDx4MB07diQuLq7cMVu3buWPP/5g7dq1T2W7RCJ58ZGOuxSKN+OFwvogRdy4cQMXFxfc3NywsrLC0NCQxMRE5XqRSvHQoUP0798ftVqNp6cngwYNAgqrD77yyiuPtQ8fHx/Onz/P4cOH8fT0xMPDg5YtW5aoVZKXl6f8+/fff2flypVs3LhRsaF4TZbi90okVYGUvD8f5MvJJ+TMmTO0adOG0aNHY2pqyk8//YRa/b//ELt06YKfnx9z5swhKysLCwsLNm/eTF5eHvfv32fUqFGcOnWqwnXy8/MZNGgQ+vr6fPzxxzg4OJCYmIi+vj4qlYqcnBzu3LnDb7/9BhT+QZk+fTrffPMNhoaGyjx6enpcunQJQInTSyRVxd3MbP755+4TfenqaFU8saRc5In7CbG0tOT8+fPY2toihMDc3JyLFy+WuMfc3JyePXuyePFiPD09uXr1Kk5OTuTn5+Ps7EzPnj0rXEdbW5tPP/2UsWPHUqdOHQwMDAgMDMTAwIB+/frx3nvv8eqrr/LWW28B8O2333L//n38/PyUPyQff/wxrq6ufPbZZ9jZ2WFhYUHTpk2r/qFIJJJqRSonJRUiX05WD7XFVmlnxciXky8g165dY/LkyaVe8/f3VyoVSiQSSWnUyBj34cOH6du3L9OmTavUPB06dAAgLCxMSdt7HOLi4nB3d3/qdV977TV27dpV6pepqSkqlQpvb+8qWUsikbx81MgT9549e/Dw8MDFxaVK5nN1da2SeaqK69evo1Kpnvc2JJJyeZo6JSCzSqqCp3bccXFxfPfdd9StW5c///yTDh068PnnnzNu3Dh+/vlnoGTn9D59+jBgwAASEhIwNDRk2LBhhIaGkpKSQmBgID169HhkjYMHD7J48WIKCgpo3bo1c+bM4eDBgxw4cIDY2Fg0NTUZMWJEiTERERGsWLECDQ0NTE1NmTNnDkOGDCm183oRxfdpaWnJ4MGD+e2339DS0mLx4sW0bt2aX375hfnz51OnTh3atm2rjL169Sp+fn7cuXOHunXrMmvWLN588028vLy4c+cOV69exdPTk+PHj/Prr7+iqanJO++8g4eHR5nP1t/fn+TkZGbPns2QIUNIT09nwoQJXLt2jbZt2xISEsLNmzcZP348+vr61K1bl++//75MheaqVauIjo5GrVZjaWmJp6en0kZNInlanqZOCchaJVVBpU7cJ0+eJDo6mmbNmvH+++/zyy+/lHlvWloaVlZWzJkzB3d3d3766Sc2bNjAjh07WLt27SOO+9atW/j6+hIWFoaRkRHff/89c+bMISQkhN9++62EjLuI1NRU5s+fz/bt22nRogWenp4cOXIER0fHUjuvl8Y///xDr169mDVrFoGBgaxfv56pU6fi5eX1/9g797ie7/7/3ztjIaQcwnbFNIfESA3RYVikVUpMDrOxTbSxz5RDKoaLLBJhdm10MXNIVMKQw6yEizVfhEtMpkhyKFSfPr8/+vW+Op91+rzut9tut6vP+/1+vV/P9+fy7NXr/Xw8H2zduhVDQ0MWLFggnT9v3jy8vb3p0aMHN2/eZObMmRw+fBjIK8XbuHEj9+7dk0Q7L168wMvLi1evXpU6h4ULFxIUFMTixYs5e/Ysf//9Nxs3bqRjx464uLjw+++/07VrVxITE9myZQsGBgalKjQzMzO5fPkye/bsQUVFBZlMxoEDB4q585SFkLzXHsoSq4izelQrcXfr1o127doBYGhoyJMnT8o838LCAqBQGVu+GrEo8fHxGBsbS532xo0bx+bNm8sc/+LFi/Tr10+aU77lmJGRUYnO66UxZMgQKb7z58+TkJCAnp4ehoaGQJ5L+9q1a8nIyODy5ct4eXlJ12ZmZvL48WMgr6Yb8hpHaWlp4erqiqWlJV9//XWpSbskjIyM6NSpE5D3nPPHb9OmjfR8SlNoJiUlER8fL/2Se/nyJR06dKjwvUFUldQWDS3W6iSlhhRnVam3VSUFk0/+n94lOafno6mpKf3vgmpEyFstT58+HQA9PT1cXV0LHVcoFOTk5JR5zbhx4wptAaSlpQGlO6+XF1e+6rCoI3z+3HNzc9HU1JRc2iGvV4mOjg4ATZo0AfJqsnfv3k1cXBynTp3C1dWVkJCQQlsuZVHwGRacS/74QKkKzdWrVzN58mSmTp0KwNOnT4s9e4FA0LCo0ZeTzZs3L9E5vSLo6+sXSoCpqaksXryYpKQkDAwM+OWXX4oJV4pek5KSgq+vLw8fPpRMfwcOHIizs3OJzusVpXv37qSmpnLt2jWMjIyIjIyU4n3zzTfZv38/9vb2nDlzBm9v72IKxStXrrBkyRJCQkIwNzfnypUrkot8SaipqRX7JVUe+QpNS0tLsrKymDBhAr6+vpiZmREYGIiLiwtaWlrMnDkTBweHYttMAkFlqYrcHcTLyZqgxhN3Sc7pVUFXVxc/Pz/c3d3Jzs6mQ4cOfPvtt2Veo6+vz4IFC5g2bRq5ubmYmJhICao05/WKoKGhwXfffYdMJkNdXZ0ePXpIx1atWoWPjw9btmxBQ0ODgICAYi/+evTogYmJCaNHj6Zp06b069dP2jYqCUNDQ549e4ZMJmPs2LEVmqOrq2upCs1r167h4uKCXC5nyJAhODg4VPoZCARFqYrDOyjP/vbrRCmUk6U5rwsqhtjjrh2UJVYRZ/kI5SQ147xe0xw8eJBNmzaVeKzg9o9AIBAURSlW3ILqIVbctYOyxCriLJ/yVtwNUvIuEAgEyozSJ24rKyuSkpJey9ienp6Ehoa+lrFLIr/3SmkU7IEiEFSX5i2a0rZt80r/J6pKqo9S7HEL8hA9UAQ1iZC81x2NInHXtTM7wE8//cTPP/+MmpoalpaWyGQyAE6cOMGOHTt49OgRn332GePGjSMjIwM/Pz9u3LiBXC7n008/ZfTo0Tg4OLBkyRJ69eqFXC7H0tKSffv24ezsjLGxMVevXmXHjh2cOHGCH3/8ERUVFXr27MmiRYsK2aGVNn7BHiiLFy+u8PMVkvfaQ1liFXFWj0aRuKFundnj4+PZsWMHe/fupWnTpnzyySdcvnwZgKysLHbv3s2NGzeYNGkS48aNIzg4mJ49e/LPf/6T58+f4+rqSp8+fbC3tycyMpJevXoRGxuLkZERbdq0AfLaBaxZs4aEhAQ2btzIrl27aNWqFb6+vgQFBTFv3jxpPqWNX7AHSmUQLydrh4YWq5C8l40oB6wAdenMfu7cOSwtLWnePO//yD/99JN0zNraGhUVFbp16yb1GPn99995+fIle/fulca/ceMGo0aNYty4cXzzzTdEREQwZswYaZx8mX7+vVq1agXk9XAp2CulrPEralIsEAjqN40mcdelM7u6unohtWRKSgpNmzYtNI+Cx3Nzc1m1ahU9e/YE8uT9LVu2RENDg7feeouzZ88SExODt7e3dE1+/5Tc3NxC9y6ph0tp4//nP/8pNQaBoLIIyXvdoRRVJa/bmb1///6cPHmSjIwMcnJymDt3rrRVUhJmZmbSdsyDBw8YM2YM9+/fB8De3p5//vOfDBw4UEr+BTE1NeX48eOkp6cDsGvXrmI9XEobvyo9UASC0qiKw7twea8ZlCJxDx48mNzcXGxtbXFwcOCtt94qVgJY0Jnd1dWVN998EwcHB5ycnMp1Zu/ZsycTJ07E1dUVe3t7+vfvz3vvvVfq+e7u7rx8+ZLRo0czefJkZDIZnTt3BuD999/n9u3bhbZJCmJkZMSMGTNwc3Nj5MiRPH36lC+//LJC4xfsgSIQCBouQjkpKBfxcrJ2UJZYRZzlozQvJ183wpldIBDUF5Q+cVtZWbFt2zbJSaY08p3ZK0v37t1JSEio6vSqRHx8PIcPH0YmkxEaGkpcXBwrVqyo1TkIBILXh9In7sbIzZs3efToUV1PQ9DIES7vdUejSNx1rZxMSkri888/5x//+Ac3b96kQ4cOrFq1SrIwy83NxcbGpkSneSsrK6ytrYmPj0dXVxcnJydCQkJITk5mxYoVmJqakpiYiLe3N+np6TRr1owFCxZgbGyMp6dnIdPk7t27c+7cOQIDA8nMzCQ4OBh9fX3u3LmDm5sbf//9N+bm5ixduvT1fRkCpUFI3uuORlNV8scff+Dr68vevXvZvn07rVu3JjQ0lO7duxMZGcnRo0cJCQkhIiKCYcOGsX37duna5ORk3N3dJeXkrl27gDzl5J49ezh27Bjnz58v8/7Xr19nwoQJREZGYmhoSFBQkHRMVVVVcpoHCjnNp6amYmFhQVhYGK9eveLo0aPs2LGDWbNmsXXrVgBkMhlubm6Eh4fj5eWFh4cHWVlZJc6jRYsWzJ49GysrKz7//HMgr4593bp1REVFcerUKW7cuFH1By0QCOqcRrHihrpVTgK8+eabUsnghx9+yNdff13ouKOjY6lO8/k2Zh07duTdd98tNO+MjAz++usvSQxkYmJCy5YtuXXrVoWfTf/+/aXVf+fOnSUFZ0URvUpqD2WJVcRZPRpN4q5L5SQUdmJXKBTFnNTLcprX1NQscd75YxVFoVAgl8sLOb5nZ2dXaG5FHesrgigHrB0aWqyiV0nZiHLAalJQOfny5UsCAwNp166ddDxfOTlv3jxMTU1LdUwvS4STmJjI1atXeeedd9i7d2+JZsBVcZrX1tbGwMCAI0eOMHz4cC5dukRqairdunVDR0eHmzdvAhRylhcKSUFtICTvdYdSJO7Bgwdz7do1bG1tUSgUDBgwoNg+b0HlpEwmK9UxvTRatmxJYGAgf/31F927dy/xBWBVnebzneTXrVuHhoYG69atQ1NTk/Hjx/Pll19iZ2eHmZkZbdu2BfJ+EQUFBeHv71/oBaxAUJMIl/e6Qygna4CkpCQmTZrE8ePHSz2nITvNi62S2kFZYhVxlo/YKqkhylNOlkd9dJoXCAQNE7HiFpSLWHHXDsoSq4izfITLu0AgEDQyxFZJFahof5OqYm9vX6W+KAJBbVFVuTuIqpKaQCTueohI2oL6TlXl7iAk7zWB0ibu+tDfxN3dnW7dunH16lXatGnD2rVr0dHRkToKxsTEsGrVKiCv3HD16tVoamoyZ84cUlNTAZg5cybW1tbExcUREBDAy5cvefr0KV5eXtjY2ODp6Ym2tjb/93//R0pKCjNnzsTJyen1PViBQPDaUdrEDXXrDA9w7do1li1bRo8ePZg1axbh4eG4ublJxzds2ICPjw/GxsZ8//33XLlyhYcPH9KxY0c2b97M1atXOXDgANbW1vz73/9m6dKlGBoaEhMTw7Jly7CxsZHmumPHDq5fv86kSZMqnbiF5L32UJZYRZzVQ6kTd133N2nTpg09evQAoFu3bjx58qTQcWtra9zd3bGxscHa2ppBgwZx+/ZtvvvuO1JSUhg2bBgzZ84E8kQ60dHRHDp0iD/++IOMjAxpnEGDBqGiosLbb78teVVWBlFVUjs0pFirm5AaSpzVQdRxvybqur9JvnM7lNxDZMqUKVhaWhIdHc2qVauIj4/n888/JyoqitOnTxMdHc2//vUvDh48yIQJExg4cCADBw7E3Ny8UJOr/PsUdJoXCKpDVeXuIF5O1gRKnbjLojb6m5SHs7Mzvr6+TJkyBR0dHY4dO8a///1v7t69i5eXFxYWFlhaWvL06VNu377Njh070NTUxN/fv5CLvUBQ01RV7g7Ks03yOhGJuxRqo79JecyZMwdPT0/U1dVp1qwZS5cuRVdXlzlz5mBnZ4eamhoymQwdHR3Gjh3LqFGjUFdXx8zMjJcvX5KZmVmt+wsEgvqJUE4KykXscdcOyhKriLN8xB53HSKc4QUCwetAJO4q8umnn7J06VL09fVLPacizvB3794lODiYZcuW1fQUiyEc3wWCxoFI3FWkprr8/f3339y9e7dGxhIIaoPqyN1BVJXUBEqZuDdv3kxUVBRyuZzBgwczfvx4Zs2aVaKK8eDBgwQGBtKsWTPeeecd5HI5K1askPqVxMXFcfr0aZ48ecLdu3cZNGgQPj4+Jd5HJpMVK8lbunQpSUlJ+Pr68vz5cwYMGICLiwsAbm5ufP311/j7+2NkZMT58+d59eoV8+fPZ/DgwaSmpuLt7U1ycjIqKirMnTuX9957r7Yfp0DJqI7cHYTkvSZQuu6Ap06d4vLly+zZs4ewsDBSUlIIDw/n2rVrTJ06lYiICFq0aEF4eDhpaWksW7aMrVu3smfPnmICmXwuXrxIYGAgBw4cIDo6moSEhBLvk+/yXpCFCxfSq1cvFi9ejJOTk7S1cu/ePdLS0iRvyufPn7Nv3z5Wr16Np6cnWVlZfPvttzg5OREaGkpwcDDe3t48f/789T08gUBQL1C6FXdMTAzx8fE4OjoC8PLlSxQKRYkqxvPnz9O3b19pH/vDDz8s5O2YT9++fdHWznsD3KlTJ548eVLifTp06FDm3AYOHMiiRYtISkpi//79hSzO8lfh77zzDm3btiUhIYHff/+dW7duERgYCEBOTg53794tpPCsCYTkvfZQllhFnNVD6RK3XC5n8uTJTJ06FYCnT5+SnJzMpUuXpHPyVYyqqqrk5uaWO2ZJCsiS7qOmpsbPP//Mzp07AXB1dS3UtEpFRYUPP/yQyMhIoqKi+OGHH6RjBVWdubm5qKurk5uby9atW9HR0QHgwYMHtGnTpiqPpUxEOWDt0FBirYlk1BDirC6iHLAGMTMzIzAwEBcXF7S0tJg5cyYODg4lntuvXz/8/Px48OABbdu25eDBg4WSdFXuM378eMaPHy+dd/78+UKO7I6OjkyYMIGuXbsWqlg5ePAgxsbG/Pnnnzx9+pS3334bMzMzduzYwRdffMHNmzf56KOPOHbsmLT6FwheB9WRu4N4OVkTKF3itrKy4tq1a7i4uCCXyxkyZAgDBgwo8dzWrVuzcOFCPv74YzQ1NTEwMKBFixZVvk9JvyAMDQ159uwZMpmMVatW0b59e9q3b1/s3Lt370qfBQQEoKamxsKFC/H29sbOzg6AlStXiqQteO1UR+4OyrNN8joRyskyePz4MSEhIbi7u6OqqsrSpUvp0qVLodarNYlCoeDBgwe4ubkRERGBpqYmkFdd4u7uXm0JfVURWyW1g7LEKuIsH7FVUg10dHR4+vQpo0ePRk1NjZ49e0ovCV8Hhw8fxsfHBx8fHylpV5aDBw+yadOmEo8JZx2BoHEgVtyCchEr7tpBWWIVcZaPcHkXCASCRoZI3CVw8uRJhgwZwty5c8s8LzQ0FE9Pz1qa1f+4e/cu8+fPr/X7CgSQJ3lv27Z5lf8TVSXVR+xxl8ChQ4dwd3dn3LhxdT2VEhH9TQR1iZC81z31NnGfPXuWTZs20aRJE/773//SvXt3vvrqK6ZNm8bx48cBWLduHQCzZs1i0KBBWFtbEx8fj66uLk5OToSEhJCcnMyKFSswNTUtdo/o6GjWrFlDbm4unTp1ws/Pj+joaI4dO0ZMTAyqqqo4OzsXuiYsLIzg4GC0tbXp2LEjzZo1A+DSpUt8++23vHr1ilatWuHn50eXLl1ITEzE29ub9PR0mjVrxoIFCzA2NsbT05P09HTu3LmDTCbj3LlznDlzBlVVVWxsbHB3dy/12RTsb3L+/HnWrFmDoaEhc+fORVtbG19fXy5evEhwcDCbN29m48aNHDhwADU1NQYNGoRMJisk6BEIBA2Lepu4Ia8HSFRUFHp6eri4uPDbb7+Vem5qaioWFhb4+fnh5ubG0aNH2bFjB/v27WPr1q3FEvejR4/w9vbm559/xsDAgC1btuDn50dgYCAXLlzA1NRUkqvnk5KSgr+/P2FhYejo6DBjxgyaNWtGVlYWc+bMYc2aNRgbGxMVFcWcOXPYu3cvMpmM6dOnM3z4cC5duoSHhweHDx8G8qpWNm7cyL179yRj4hcvXuDl5cWrV69KFfssXLiQoKAgFi9ejL+/PzExMRgaGnL9+nXpnNOnTzNs2DBOnjzJ8ePH2bt3LxoaGsyaNYudO3fy0UcfVfh7EJL32kNZYhVxVo96nbi7desm+TwaGhqW2uQpHwsLCwA6duzIu+++C/zPsb0o8fHxGBsbY2BgAMC4cePYvHlzmeNfvHiRvn37oqurC4CdnR2xsbHcvn2bFi1aYGxsDMAHH3yAt7c3z54946+//pIMhE1MTGjZsiW3bt0CkM7X19dHS0sLV1dXLC0t+frrryus0Bw6dCg//fQTZmZmdO3alVu3bvHo0SNOnTpFYGAgISEhjBo1iqZNmwLg5OREWFhYpRK3qCqpHRpKrELyXjGUtqqkaA8QoJATekGpOFCo9rnoVkBKSgr29vbY29vz6aefFutBolAoio1X9JqiTuzq6nm/90rqZ6JQKHj2rPiXlt/HBKBJkybSOLt378bDw4P09HRcXV1JTEwsdm1J9O3bl2vXrvH7779jamrKgAEDOHToEDk5OXTo0KHEuRWNUyAQNCzq9Yq7KM2bNyc9PZ20tDS0tbU5ffo0lpaWFbpWX1+/kAAlNTWVxYsXk5SUhIGBAb/88ksxZWJJ1yxZsoSUlBSpd0mLFi34xz/+QXp6urSKP3jwIB06dKBDhw4YGBhw5MgRaaskNTWVbt26FbrPlStXWLJkCSEhIZibm3PlyhUSExN56623SoxFTU1NSr7q6uoYGxsTEhLCxo0badu2Lb6+vtI2j5mZGcHBwYwbNw51dXX27t2LmZlZhZ6ZQFASoldJ3dPgEvcnn3zC2LFjadeuXbU8G3V1dfHz88Pd3Z3s7Gw6dOjAt99+W+41CxcuZMqUKTRt2pSuXbsCeSv9gIAAlixZwosXL2jZsiUBAQEArFq1Ch8fH9atW4eGhgbr1q0rpors0aMHJiYmjB49mqZNm9KvXz9p26ckivY3GTp0KOfOncPQ0JC2bdvy6NEjhg0bBoClpSVXr17FycmJnJwcBg8ezMSJE6v83AQC0auk7hHKSUG5iD3u2kFZYhVxlo/oVdJAET1HBAJBaYjEXUXyPSfzq1JqgoJ16ba2ttja2pZ5/tmzZwkKCiIkJKTG5iAQCOo/InELBIIKU12HdxAvJ2sCpU7cZ8+eZePGjWhoaJCUlISVlRXNmjWTfCU3b97MoUOH2L9/Py9evEBDQ4PVq1cXshtLTExkxowZrFy5kt69e7Ny5Uri4uKQy+U4OjoyZcqUMuewZcsWdu3aRatWrQrVgpek6tTV1eW3335j+fLlaGlpFao6uX79Op6ensjlcvr378+pU6f49ddfhRO8oEaprtwdhOS9JqjXddy1wR9//IGvry979+5l+/bttG7dmtDQULp3705kZCRHjx4lJCSEiIgIhg0bxvbt26Vrk5OTcXd3Z9myZZiYmLBr1y4A9u3bx549ezh27Bjnz58v9d5//vkne/fuZd++ffz4448kJycD/1N1rl+/nvDwcMlCLSsrC09PTwIDAwkNDZXqwAE8PT3x8PBg//79dOrUSaoVF07wAkHjQ6lX3ABvv/027du3B6BVq1aYm5sD/1Nc5kvRb9++zenTpws57e6FkAAAIABJREFUqHt4eNC7d2/69+8P5DnIX716ldjYWAAyMzNJSEiQjhclLi6OoUOH8sYbbwAwcuRIcnNzS1V1JiQkoKenh6GhIQAODg6sXbuW9PR07t27x9ChQ4E8deS2bdsAasQJXkjeaw9liVXEWT2UPnFraGgU+rmg4vL+/fuMGzeOiRMnYmFhga6uLlevXpWOL1iwgPXr13PixAmGDRuGXC5HJpNJEve0tDQpKZdESUrMrKysUlWdRc/Pn6uamhqlVXXWhBO8KAesHRpCrDWViOp7nDWB0kre65o///yTLl26MGXKFHr37s3Ro0elLQjI6zXi4+ODn58fmZmZmJmZsWvXLrKzs8nIyGDChAlcunSp1PHNzc2Jjo7m2bNnvHr1il9//RWAPn368Mcff5CUlAQgqTq7d+9Oamoq165dAyAyMhLIEyZ16tSJkydPAhAeHi7dI98JHuDmzZvY2dnx4sWLGnxKAoGgtlH6FXdZDB48mGvXrmFra4tCoWDAgAHcuHGj0DkDBgxg4MCBrFmzBplMxp07d3BwcCAnJwdHR8cyDX7feecdJk+ezNixY2nRogUdOnQASld1amho8N133yGTyVBXV6dHjx7SWCtXrmT+/PmsWbOG7t27S/vfwgleUJNUV+4OoqqkJhDKyUZCUFAQLi4u6OnpceTIEcLDw6W68OoitkpqB2WJVcRZPkI5Wcf89ddfzJo1q8RjS5curVa/lYJ06NCBjz/+GHV1dVq0aFFu3xWBQNBwEStuQbmIFXftoCyxijjLR7ycFAgEgkZGvUjcZ8+exc3NrdavLUh5Y6xbt67Ke8Zubm6cPXu2StcWJV/hCXnqyh9//LFGxhUISqO6ru7C5b3mEXvc/5+4uLi6nkKluXz5cl1PQaAE1ITMvSBC8l596k3ifvz4MdOmTePBgwcYGxuzePFizpw5U+F+HXfu3GHy5MkcP34cVVVVzp49y/fff8+WLVsK3ScmJoZVq1YB0LJlS1avXs2GDRsAcHZ2xsXFhdjYWFavXg3krbSL+j/m+znm5ORgYGDAkiVLaNWqVZnx7dmzhxUrVvD06VMWLFiAlZUV169fZ8mSJWRmZpKWlsb06dMZP34869at49KlS9y/f5+JEydiYmLCggULADAyMgLyarJ37twJ5L2YtLW1ZeHChSQkJKCiosK0adP48MMPCQ0N5cSJEzx69IiHDx9iaWmJp6enZAUnEAgaHvUmcSclJREUFESXLl346quv2Lx5M7/88ksxF3Z/f388PT3ZunUrhoaGUkLr0qULBgYGnD17FnNzc8LCwoq5tANs2LABHx8fjI2N+f7777ly5QoLFy4kJCSE3bt3k5GRQUBAAM+fP0dbW5uIiAi2bdsm9SFJS0tj9erVbNu2jZYtW7Jz5078/f3LreJo3rw5+/btIzo6mqCgIKysrNi9ezdffPEF5ubm3L17lzFjxjB+/HgAsrKyOHjwIJBnSuzp6cmgQYNYv349Z8+epWvXrri6ugJ5EveVK1fSqlUrIiIiSEtLw9nZWUryFy5cYP/+/bRo0YJJkybx66+/SurOiiAk77WHssQq4qwe9SZx9+/fnzfffBP4X6IyNTWtcL8OyEtgBw4cwMTEhNjYWHx8fIrdx9raGnd3d2xsbLC2tmbQoEGFjr/xxhsMHTqUX3/9lU6dOtGpUyf09fWl43/88Qf3799n0qRJQJ6kvGXLluXGZ2NjA0DXrl15/PgxkNcY6vTp02zatInr16+TmZkpnZ/fJTAtLY0HDx5I83R0dGTv3r3Fxo+NjWXZsmUAtG7dGmtra+Li4tDW1sba2lpypre1tSU2NrZSiVtUldQO9TXW15F86mOcNY1S1HHnO6ZDXm+Oon/Kl9evA/KaNAUEBHD48GEsLCzQ0tJi7dq1HD9+HIDZs2czZcoULC0tiY6OZtWqVcTHx/P5558XupeTkxPBwcEYGBgUW7XL5XL69evHxo0bAXj16hUZGRnlxpc/z4Jxffnll7Ro0QJLS0tsbW2JiIiQjuUrH8uKt+jzKfpzvjy/4DW5ubmljiEQCBoG9SZxX7hwgb///pt27doRFhbGJ598wrZt24q5sBfs12FkZCT16wBo2rQpFhYWfPfdd1IFiIeHBx4eHtI5zs7O+Pr6MmXKFHR0dDh27BjwP+d0dXV1+vfvT3JyMvfu3ZO2YvLp06cPCxculFzYN2zYQEpKCitWrKh0zGfOnCEqKgp9fX2pXWzBXiiQ17GwQ4cOUiOrgsldTU2NV69eAXk9Sfbs2cPChQtJS0vj2LFjrFu3joSEBE6fPs2zZ8/Q1NQkMjKS2bNnV3quAuWlJmTuBRFVJdWn3iTurl27Mn/+fB4+fIiZmRnTpk2ja9eulerXATBq1Cj+85//0KdPnxLvM2fOHDw9PVFXV6dZs2YsXboUyNtCsbe3JzQ0FC0tLd5//33S09OLObK3bduWZcuW8eWXX5Kbm4u+vr70srOyzJo1iwkTJqClpYWRkREdO3aUGksVZNWqVXh5ebFmzRpMTEykzwcMGMC8efPQ1dVl5syZ+Pj4YGdnh1wu57PPPqNnz54kJCTQunVrPv30Ux4/fsyYMWMYMmRIleYrUE6q6+peFGXZ336dNCrlpFwuJyAggDZt2jB16tQqjaFQKMjOzmbq1KnMnz+fnj171vAsa5fQ0FDi4uKq9BdBPmKPu3ZQllhFnOXTYPa4awInJydatWpFcHBwlcd4+PAho0aNwtnZuVJJe+7cudy8ebPY51ZWVoW2agQCgaC6NKoVt+D1IFbctYOyxCriLB+lWnELBIKqUxMO7hVBvJysPg0ycefXeBcs1Tt27BiXL19+7dsS+dUqpbVqrShJSUlMmjRJKlWsDPlmwZMnT67UXn5N7HcLGi81LW0vDSF5rz4NMnGXhLW1NdbW1nU9jVph//79/Pjjj5LcXyAQKBcNInErFApWrFjBiRMn0NPTQy6XY2pqysiRI2nVqhVNmjTBzs6OuLg43n//fXbv3i0JZEJCQrhz5w5eXl6sXLmSuLg45HI5jo6OTJkypdB90tLSsLe35/Tp0wAMGTIELy8vbG1t2bRpkyRciY+Px9XVlZSUFBwdHZk1axZyubzU8Tdv3kxUVBRyuZzBgwcjk8kK3Tc8PJwtW7agpqaGgYEBq1atKtYfJR9vb29SUlKYOXMmnTt3xsLCggkTJvDLL7/w008/ERUVRXZ2NjY2Nhw9epTIyEiCg4PR1tamY8eONGvWrNLPX0jeaw9liVXEWT0aROI+fPgwV65cISIigmfPnjFmzBgAEhMT2bJlCwYGBoSGhgJgYWHB4sWLefLkCS1btiQyMpL58+dLvUb27dtHVlYW06ZNo1evXvTv31+6T+vWrWnfvj3Xr19HTU0NuVxOXFwctra2nD59Gl9fXw4ePMijR4/YuXMnz58/x8rKiqlTp0oGvUXHz8zM5PLly+zZswcVFRVkMhkHDhzg3Xffle67Zs0adu3aRZs2bfjnP//JrVu3eOedd0p8Fn5+fvz2229s3ryZW7dusWfPHiZMmEBsbCxPnjwhNTWVmzdv0rdvX9LS0vD39ycsLAwdHR1mzJhRpcQtXk7WDnUda20mU2X4TpX+5WRcXBzDhw9HQ0OD1q1bY2FhAUCbNm2kXib5aGho8P7773PkyBEGDRpEeno6xsbGbNmyhatXrxIbGwtAZmYmCQkJhRI35CX+mJgY1NXVmTRpEpGRkTx79ozU1FSpP8qQIUPQ1NSkdevWtGrViidPnhATE1Pi+ElJScTHx0v78S9fvqRDhw6FErelpSXjx4/HxsaGESNGlJq0izJw4EAWLVqEXC7n1q1b2Nracu7cOf7880+GDRvGxYsX6du3r9SnxM7OTpqfQCBouDSIxF20X0d+X5P8fh5Fsbe3Z+3atTx58kRyN5fL5chkMqm5UlpaGm+88UaxXibDhg0jKCgITU1NPDw8iIqKIjw8nMGDBxe7f8G5lTb+6tWrC71EfPr0KWpqalKjKchzYr927RonT55EJpPh7u6OvX35L3C0tLR45513CA8P5x//+AcDBw4kJiaGCxcu8Mknn3Du3LkSn5tAUBI1LW0vDVFVUn3qhQNOeZibmxMVFUVWVhZPnjyR9qBLw8TEhAcPHrB//35pW8XMzIxdu3aRnZ1NRkYGEyZM4NKlS3h4eLB//37279+PtbU1PXv2JDExkdu3b2NoaMjAgQMJDg7G0tKyzHuWNr6ZmRn79+8nIyODnJwcZs6cyeHDh6XrcnJyGD58OK1atWLGjBnY29tz9erVCj+boUOHsn79ekxNTTE1NeXYsWM0a9aM1q1b8+6773Lp0iVSUlLIzc2V2sQKBCXx7OkLHj589tr/09QQTc6qS4NYgtnY2PDnn38yevRodHV1pS2Lsvjggw/47bff6NSpEwCurq7cuXMHBwcHcnJycHR0ZODAgcWuU1FR4d133+XFixdAXkLevXs3AwYMKPN+ZY1/7do1XFxckMvlDBkyBAcHB+7duwfkrYJnz57Nxx9/jJaWFm3atKlUud6wYcPw8fHB1NSUli1b0qZNG4YNGwaArq4uCxcuZMqUKTRt2pSuXbtWeFyBQFB/EcpJQbmIl5O1g7LEKuIsn0bxclLZ+Ouvv0oV+CxdupTevXvX8owEAkF9QiTuamBlZcW2bduKVbZUFjc3N0JCQqSfO3fuzP79VVewPXv2DE9PT9avX1+teQkaP7Ulcy+IeDlZfUTirgfUtMP8kydPKvWCU6C81JbMvSBC8l59GkRVyevk7NmzTJ06lenTp2Nra4u/vz8bNmzA0dERR0dHUlNT+fe//42zszOjR4/GwcGBW7duFRojMTGR4cOHc+nSJeRyOcuXL8fBwYExY8bw008/lXn/fCMHZ2dnAKKjo7G3t8fOzo4vvviC1NRUYmJiJGNgyOs5snjxYnJzc1m6dCmjRo1i9OjRbN68WRrzwYMHzJw5E4CAgABcXFwYMWIEbm5upKam1tTjEwgEdYBYcZNnABwZGYmOjg7vvfce8+bNIzQ0FC8vLyIjI4mOjiYkJIQmTZqwdu1atm/fzqJFiwBITk5m8eLFLFu2DBMTE37++WegbIVmQQo6zD969Ahvb+9izvZr165l4cKF/PXXX3Tu3JmwsDDmzp3Lzz//zP379zlw4ABZWVm4ubnx9ttvs3DhQiZNmsT69eu5c+cOt27dYufOnaiqqvLNN99w4MABPv744wo/HyF5rz2UJVYRZ/UQiRt4++23ad++PZDn8Whubg5Ahw4dePr0KatXryYyMpLbt29z+vTpQspGDw8PevfuLSXm0hSUpSXugsTHx2NsbFzM2V5FRQUHBwcOHDiAo6Mjjx49ok+fPvzwww84ODigpqZG06ZNsbOzIyYmplDZX5cuXZg3bx67d+8mMTGRS5cu0blz50o9H1FVUjvURax1lUCV4TsVVSWvGQ0NjUI/F3RBv3//PuPGjWPixIlYWFigq6tbaP94wYIFrF+/XjLzLU1BWRFyc3ML/ZzvbA/g4ODAJ598gqampqSqLOn8ombDly9fZu7cuUyZMoURI0agqqpazBFeIBA0LETiLoc///yTLl26MGXKFF6+fElgYCDt2rWTjhsbG+Pj48O8efMwNTWVFJSWlpZkZWUxYcIEfH19SxT75JPvMN+nTx8WL15czNkeoGPHjrRr146dO3dK2zFmZmaEhYVJ9woPD+ezzz5DXV1dSvjnzp3D1NSU8ePH8/jxY06cOCH9UhEIakvmXhBRVVJ9ROIuh8GDB3Pt2jVsbW1RKBQMGDCAGzduFDpnwIABDBw4kDVr1iCTySqk0CxIQYd5Pz+/Ys72+dja2nLkyBH09fWBvK2U27dvY29vT3Z2NnZ2drz//vvStW5ubvj7++Pu7i71bOnVq1eJTvIC5aSmHdwrgrLsb79OhHKygZCTk8M333zDyJEja33FLPa4awdliVXEWT5ij7seUF0lpEKhYMiQIbz33nvY2Ni8jikKBIIGhEjctUB1lZAqKirExMTU4IwEAkFDpt4KcM6ePYubm1utX1uQ8sZYt26dZB4sEAgEtYVYcZdBTUvRBYKiFO0Vogwv7kRVSfWp14n78ePHTJs2jQcPHmBsbMzixYs5c+YMa9asITc3l06dOuHn54euri6//fYby5cvR0tLS3I/v3PnDpMnT+b48eOoqqpy9uxZvv/+e7Zs2VLoPjExMaxatQqAli1bsnr1ajZs2ADkSdFdXFyIjY1l9erVQN5Ku6iZ76lTpwgMDCQnJwcDAwOWLFlCq1atSozr8OHDREVFsWbNGhITExk5ciRnzpxBV1eXadOm4eHhwcuXLwkICODly5c8ffoULy8vbGxsSE5O5uuvv+bJkye8/fbbnDt3jlOnTvHixQsWLlxIQkICKioqTJs2jQ8//JDQ0FBOnz7NkydPuHv3LoMGDcLHx6cmvyZBNaiLXiF1jehVUn3qdeJOSkoiKCiILl268NVXX7F582Z++eWXYpJwf39/PD092bp1K4aGhixYsADIUw0aGBhw9uxZzM3NCQsLk7wfC7JhwwZ8fHwwNjbm+++/58qVK4Wk6BkZGQQEBPD8+XO0tbWJiIhg27ZtkgFxWloaq1evZtu2bbRs2ZKdO3fi7+9fqJSvIIMGDWLp0qUoFApiY2Np06YNcXFxWFlZkZiYSO/evfHw8GDp0qUYGhoSExPDsmXLsLGx4dtvv+WDDz7go48+4tdffyUiIgLI+2XSqlUrIiIiSEtLw9nZGSMjIwAuXrxIREQEampqjBw5kvHjx9O9e/cKfw9C8i6oaZTlO1VKyXv//v158803gTyjW09PT0xNTYtJwhMSEtDT05OccRwcHFi7di0ATk5OHDhwABMTE2JjY0tcbVpbW+Pu7o6NjQ3W1tYMGjSo0PE33niDoUOH8uuvv9KpUyc6deok1VJDXq+T+/fvM2nSJCBP0diyZctS49LW1uatt94iISGB2NhYJk+ezLlz53jjjTcwMzNDRUWFVatWER0dzaFDh/jjjz/IyMgA4MyZMyxfvhyA999/nxYtWgAQGxvLsmXLgDy3emtra+Li4tDW1qZv375oa+cl306dOvHkyZOKfwmIcsDXibIksKI05u80H6UtByxobqtQKFBRUSl0PF8SXtRMuKBkfeTIkQQEBHD48GEsLCzQ0tIqZhA8ZcoULC0tiY6OZtWqVcTHx/P5558XupeTkxPBwcEYGBgUW7XL5XL69evHxo0bAXj16pWUaEtj2LBhnDlzhlu3buHj48OkSZNQVVWVvC0nTJjAwIEDGThwIObm5nz99ddSbCWV3hf9rKD8veC2TtFnJRAIGh71OnFfuHCBv//+m3bt2hEWFsYnn3zCtm3biknCu3fvTmpqKteuXcPIyIjIyEhpjKZNm2JhYcF3330nVYB4eHjg4eEhnePs7Iyvry9TpkxBR0eHY8eOAf+Toqurq9O/f3+Sk5O5d++etBWTT58+fVi4cCGJiYm89dZbbNiwgZSUlDK9I4cOHcpnn31Gr169aNWqFRoaGkRHRzNnzhzS09O5ffs2O3bsQFNTE39/fykJm5ubEx4ezoQJEzh58iRPnz4F8uTve/bsYeHChaSlpXHs2DHWrVtHQkJCzXwZgtdCXUjO6xrxcrL61OvE3bVrV+bPn8/Dhw8xMzNj2rRpdO3atZgkXENDg++++w6ZTIa6ujo9evQoNM6oUaP4z3/+Q58+fUq8z5w5c/D09ERdXZ1mzZpJPbILStG1tLR4//33SU9PR1NTs9D1bdu2ZdmyZXz55Zfk5uair68vvewsDUNDQxQKBaampgCYmppy48YNqSHV2LFjGTVqFOrq6piZmfHy5UsyMzNZsGAB8+bNY9euXRgZGUlbJTNnzsTHxwc7OzvkcjmfffYZPXv2FIm7nlNQct7Yt4XyUdbtoZqk0Uve5XI5AQEBtGnThqlTp1ZpDIVCQXZ2NlOnTmX+/Pn07NmzhmdZcbZt28Z7771H165d+b//+z8WLVpEaGjoa72n2OOuHZQlVhFn+TToPe6awMnJiVatWhEcHFzlMR4+fMioUaNwdnauVNKeO3cuN2/eLPa5lZVVoa2aytClSxfmzJmDqqoqWlpaLFmypErjCASChkujX3ELqo9YcdcOyhKriLN8yltx11vJe3U4efIkQ4YMYe7cudUaJ7/W+eeff5Z6YFeEmpLcl4ZcLmfatGmMGDGCH374odjLUoFA0LhplFslhw4dwt3dnXHjxtXIeOPHj6+RcWqKlJQUEhIS+O233+p6KoIKUFTWXhbK8OJOVJVUn9eWuM+ePcumTZto0qQJ//3vf+nevTtfffUV06ZNk2qo88vzZs2axaBBg7C2tiY+Ph5dXV2cnJwICQkhOTmZFStWSNUXBYmOji4mf4+OjubYsWPExMSgqqoquafnEx4eTnBwMCoqKvTu3Rs/Pz9GjhzJDz/8wFtvvUVmZiYffPABR44cka4pOM/BgwczYsQILly4gJqaGmvWrKFTp04lSu4hT3bv4+NDeno6TZo0YdGiRfTo0QNPT0/S09O5c+cOMpmMc+fOcebMGVRVVbGxscHd3b3UZztjxgzS09NxdHRk3rx5BAUFsWDBAmQyGeHh4QAcP36c3bt3ExwczObNm4mKikIulzN48GBkMlmxmnjB60MZZe1loWzlj6+D17rivnjxIlFRUejp6eHi4lLmCjE1NRULCwv8/Pxwc3Pj6NGj7Nixg3379rF169Ziibs0R/TAwEAuXLiAqalpMaFMSkoKy5cvJzQ0lHbt2iGTyTh16hQffvghBw4cwMPDgyNHjjBs2LBivUjyefjwIebm5ixatIgVK1awfft2qZywqOQeYN68eXh7e9OjRw9u3rzJzJkzOXz4MAA6Ojps3LiRe/fuSYbEL168wMvLi1evXpU6h+DgYCZNmkRoaChnz54FwMjICBUVFa5fv87bb79NZGQkY8aM4dSpU1y+fJk9e/agoqKCTCbjwIEDkm9lRRCSd0FNoyzfaYOUvHfr1k3yZzQ0NCxXam1hYQHk+Su+++67wP+c1otSmiN6WVy8eJF+/fpJc8qvtTYyMmLq1Kl4eHiwb98+5syZU+Y4Q4YMkeI7f/58qZL7jIwMLl++jJeXl3RtZmYmjx8/BvL8KgH09fXR0tLC1dUVS0tLvv7661KTdlmMGTOGyMhIOnfuzLlz51i2bBlr1qwhPj5e+iX28uVLOnToUKlxxcvJ6qEsSaoyNPTvtCI02HLAolJrKCzNzlcl5lNQ2FJQtg55q+Xp06cDoKenh6ura6HjBR3RS7tm3LhxhbYI0tLSADAwMKBDhw4cOXKER48elSrUKRpXvny8NMl9bm4umpqahUwUkpOT0dHRAaBJkyZAnrR/9+7dxMXFcerUKVxdXQkJCSm05VIR7OzsmDx5MkZGRgwePBgtLS3kcjmTJ0+WatifPn1a7NkKBIKGRa2+nGzevDnp6emkpaWhra3N6dOnpd4c5aGvr18oAaamppbqiF7aNSkpKfj6+vLw4UNJ7Thw4ECcnZ1xcnJi6dKlTJw4sdJxlSa5b968OW+++Sb79+/H3t6eM2fO4O3tzdGjRwtdf+XKFZYsWUJISAjm5uZcuXJFks9XBn19fdq3b8/mzZuZN28ekCeFDwwMxMXFBS0tLWbOnImDg0OJXRIFrwdllLWXhXg5WX1qPXF/8sknjB07lnbt2pXrtVgWurq6ZTqil4S+vj4LFixg2rRp5ObmYmJiIiWw4cOHs2jRokrt/eZTluR+1apV+Pj4sGXLFjQ0NAgICCj2YrBHjx6YmJgwevRomjZtSr9+/aRto8pib29PQECA9E7AysqKa9eu4eLiglwuZ8iQITg4OFRpbEHVqKiTemPYFqoIYuuo+ggBDnnbLKdOneLnn3+WOvwJ/ofY464dlCVWEWf5KL3kvSIsW7aM6Ohovv/++7qeisTBgwfZtGlTiceqYzwsEAgaPmLFLSgXseKuHZQlVhFn+dS65L2xu7O7ublJtdMVIT4+vtwWr1ZWViQlJVX7XpUdXyAQNEwa5VZJfXJnv3nzJo8eParraQjqECF5L4yoKqk+ryVxN1Z39nz27NnDihUrePr0KQsWLMDKyorr16+zZMkSMjMzSUtLY/r06YwaNYrAwEAyMzMJDg7m448/xtfXlwsXLqChocEXX3yBra0tAOvXr+fq1au8ePGClStXSrXku3btkjwmvby8GDhwICkpKcyfP59nz57x4MEDHBwc8PDw4NWrV6WOD5CYmMiMGTNYuXIlJiYmVfpuBZVHSN4LI0ojq89rSdyN1Z09n+bNm7Nv3z6io6MJCgrCysqK3bt388UXX2Bubs7du3cZM2YM48ePZ/bs2cTFxfH555+zZcsWMjMziYqK4tGjR0yZMgUbGxsgz+1n+fLl/Pvf/+aHH34gMDAQgGbNmhEWFsa1a9eYPn06R48eJSIigtGjR+Pg4MCzZ88YOnQobm5uhIaGljp+cnIyixcvZtmyZZVO2kLyLqhplOU7bVCS98bqzp5PwWSbL1/39PTk9OnTbNq0ievXr5OZmVnsunPnzuHi4oKqqipt27Yt5I1ZcMz8XiaQZ2EGebL8Nm3acOvWLaZNm0ZsbCw//PADN27cIDs7mxcvXpQ5voeHB71796Z///7lxlcU8XKyeihLkqoMDf07rQgNrhywMbuzF5xnwbi+/PJLWrRogaWlJba2tkRERJT4XApec+fOHdq3b1/qmEWfSW5uLurq6qxYsYK7d+8yevRobGxs+P3331EoFGWOv2DBAtavX8+JEycYNmxYuTEKBIL6y2tJ3I3Znb00zpw5Q1RUFPr6+mzfvh3I+8WQPxeAAQMGcPDgQSwtLUlLS2PixIlERUWVOW54eDi9evXizz//JCMjgy5dunDmzBl8fX3p168fJ06cICUlhdzc3DLysLg3AAAOOElEQVTHNzY2xsfHh3nz5mFqakqzZs0qHaOgagjJe2HEy8nq81oSd2N2Zy+NWbNmMWHCBLS0tDAyMqJjx44kJSVhbGxMUFAQ/v7+zJ49m6VLlzJmzBgAFi1ahLZ22fvHmZmZfPjhh6iqqrJ69Wo0NDSYMWMG33zzDU2aNKFdu3b06tWLpKQkJkyYUOb4AwYMYODAgaxZs4b58+dXKU5B5RGS98KIraPqU28FOI3Nnb0hI/a4awdliVXEWT4NVvLe2NzZBQKBoKaotytuQf1BrLhrB2WJVcRZPkrp8l6Uxu76XhpVaVErEAjqP/V2q6Qmaeyu76UhugjWLZWRuuejDC/uRFVJ9am1xC1c3/N4Ha7v165dw9vbm5ycHLS0tFi+fDlvvvkm3bt3JyEhgVmzZmFoaMiXX37Jxo0buXr1qiR0Erw+hNS9ZERpZPWp1a2Sixcv4u3tTVRUFH///XeFXN/DwsJ49eqV5Po+a9Ystm7dWuz8fNf39evXEx4eTr9+/fDz88PZ2RkrKytmz55dLGnnu77/61//IjIyErlcXsj1Haiw63tYWBgDBgxg+/btZGVl4enpSWBgIKGhoZK3JOS5vstkMvbt28eSJUv46quvpGM6OjpERUXRvXt3Tp06xYEDB/j555+5efMmr169KvVZbd26lalTpxIaGoqLiwuXLl0qdNzHx4fQ0FAOHz7Mrl278PX1LXUsgUBQ/6nVrRLh+v56XN+HDh2Kn58fp0+fxsrKqpiPZ5s2bfD09GT27Nls2rRJMiuuKKJXiaCmUZbvtEH1KikN4fr+elzfR44cSd++fYmOjuann37ixIkTkhgpn1u3btGmTRsuX75cacm7qCqpGsqSnKpCQ/1OK0OjreMWru814/r+5ZdfMnr0aFxdXTE0NJTawOZz9epV9u3bR2hoKBMmTMDGxgYjI6NKxyWoHELqXjLi5WT1qfPELVzfq+/6/tlnn0lNpDQ0NAp1UszOzsbT0xMvLy/atWvHN998w7x589izZw8aGhqVjk1QcSoqdc+nIf91URnEXyLVRwhwSkC4vhdGbJXUDsoSq4izfOr1Vkl9Rbi+CwSC+oxYcQvKRay4awdliVXEWT6NQvLe2J3jq4OnpyehoaG1fl+BQFB3iK2SClKfnOMFtUtVpOtVRRle3ImqkurTYBJ3Y3aO//HHH9m3bx+qqqoYGxvj5+eHXC5n5cqVxMXFIZfLcXR0ZMqUKSgUClasWMGJEyfQ09NDLpdL8v+AgABiYmJ48uQJenp6BAQEoKurW6osX1AxhHS9ZhElktWnQWyVQJ5z/KJFizhw4AAZGRls3ry5RIl7aXLzgs7xQLnO8aGhobz33nuSczzA7t27sbW1JSYmhufPnwMQERFRqGQw3zn+hx9+ICwsjMGDB+Pv719qXHK5nE2bNrF3715CQ0PJzs4mJSVFcqLft28fe/bs4dixY5w/f57Dhw9z5coVIiIiWLt2LX/99ReQ94vp1q1b7Ny5k8OHD9O+fXtJtl+SLF8gEDRcGsyKu7E6x6upqdG3b1/Gjh2LtbU1U6dORV9fn5iYGK5evUpsbCyQJ41PSEjgv//9L8OHD0dDQ4PWrVtL9d1dunRh3rx57N69m8TERC5dukTnzp2l+xSV5VcGIXkX1DTK8p02Csl7dWjMzvEbNmzg0qVLnDp1ik8++QR/f3/kcjkymYzhw4cDeSv5N954g5UrVxaKL/+5XL58mblz5zJlyhRGjBiBqqpqofOKyvIrg7JXlShLkqlN6vo7rQ1EHTeN1zk+LS2Njz76iD179tC3b1+Sk5NJSEjAzMyMXbt2YWlpSVZWFhMmTMDX1xdzc3N++OEHXF1defHiBadPn8bExIRz585hamrK+PHjefz4MSdOnJCSvqB6COl6zSJeTlafBpO4G6tzfOvWrRk3bhxjx46ladOmvPXWWzg5OaGhocGdO3dwcHAgJycHR0dHqffKn3/+yejRo9HV1ZW2hGxtbXF3d8fOzg5Acn4XVJ/KSterSn3466I2EH/BVB+lEuAI5/iqoexbJbWFssQq4iyfRrNVUhMI53iBQNAYUKoVt6BqiBV37aAssYo4y6dRSN4rSmN3c5fL5UybNo0RI0ZI9egCgUD5aFRbJY3dzT0lJYWEhIQyvTrritcpC1eml1nKEKuoKqk+Nf4vTbi55/E63NxnzJhBeno6jo6OhIaGsnHjRg4cOICamhqDBg1CJpNJ4p+SnrWZmRm9evXi4cOHNW6kIGThgooiSiurz2vZKhFu7q/HzT04OBg9PT1CQ0M5efIkx48fZ+/evezbt487d+6wc+fOUq+FvH4vn376Kfv37xfuNwJBA+a1/G0r3Nxfj5t7QWJjYxk1ahRNmzYF8ipmwsLCGDp0aJnXlWd8XBJ1LXkXND6UYUsIGpjkXbi5vx4394Lk5uYW+6wkyX/RZ13wr4KKUpGqEmX5hyioGURVSdnUi6qSgm7uWVlZnD59usLX5juz79+/n++//54+ffrwxx9/SKrAstzc86/p3bs3ly5d4uHDh0CeNVm+lD3fzX3MmDGVjqugvB4o0c0d4MyZM3z00UfFrr9y5QoTJ05kwIABzJs3D0NDQxITEyt0bzMzMyIjI3n58iU5OTns3bsXMzMzWrRoUeVnLRAIGga1UlUi3Nxrxs29IJaWlly9ehUnJydycnIYPHgwEydORF1dvcaedWUQ/TwEFUVUlVQfpRbgCDf3ivH4cUadCXDatNHm0aPndXLv2kZZYhVxlo+qqgqtWr1R6nGlTtzffvut5OZekX3l2kC4uQsEgvJQ6sQtEAgEDZFGJXkXCAQCZUAkboFAIGhgiMQtEAgEDQyRuAUCgaCBIRK3QCAQNDBE4hYIBIIGhkjcAoFA0MAQiVsgEAgaGCJxC+qcK1eu0KtXL+nnp0+fMn36dD744AM++ugjqTlYVlYWMpmMDz74AAcHB/773/8Cea0L/vnPfzJy5EhsbW25cOFCncRRFhcuXGDs2LHY29szefJk7t27BzTOWEsjPDwcW1tbhg8fzvbt2+t6OlUiKCiIUaNGMWrUKFauXAnA77//jp2dHcOHDycgIEA69+rVqzg6OjJixAgWLFggdTH9+++/+eijjxg5ciSff/45GRkZlZ+IQiCoQzIzMxWurq6Kt99+W/rM19dXsWnTJoVCoVDs27dP4eHhoVAoFIotW7YoFi1apFAoFIq4uDiFs7OzQqFQKKKiohSffvqpQi6XK27duqV4//33FdnZ2bUcSdlYWloqrl69qlAoFIrdu3crPvvsM4VC0ThjLYnk5GSFpaWl4vHjx4qMjAyFnZ2d4saNG3U9rUpx5swZxbhx4xSvXr1SZGVlKSZNmqQIDw9XDB06VPHXX38psrOzFR9//LHixIkTCoVCoRg1apTi4sWLCoVCofDy8lJs375doVAoFNOnT1dEREQoFAqFIigoSLFy5cpKz0WsuAV1yooVK5g8eXKhz06cOIGdnR0Ao0eP5tSpU2RnZ3PixAmp/e6AAQNIS0vj77//5uTJk9ja2qKqqspbb71F+/btuXjxYq3HUhpZWVl4eHhgZGQE5LUDvn//PtD4Yi2N33//HTMzM3R0dGjWrBkjRozg0KFDdT2tStG2bVs8PT3R1NREQ0MDQ0NDbt++TZcuXejUqRPq6urY2dlx6NAh7t27x8uXLzExMQHA0dGRQ4cOkZ2dzblz5xgxYkShzyuLSNyCOuPYsWO8fPmSkSNHFvr8wYMHtG3bFsgzm9DW1iYtLa3Q55D3Dyk5OZkHDx6gp6dX7PP6gqamptQ2ODc3l6CgIGxsbIDGF2tpFI1HT0+PlJSUOpxR5enWrZuUiG/fvk1UVBQqKiolxlXS95eSksLjx4/R1taWzE3yP68sjcrlXVA/iYqKYvny5YU++8c//sHz58/56aefyr1eoVCgqqoquQ4V/Tw3N7fEz+uC0mL96aefJI/SnJwcZsyYUeL1DSnWylDSvIv2p28o3LhxgxkzZvDNN9+gpqbG7du3pWP5cZUWb0lxV+U5iMQteO188MEHfPDBB4U+2717N5s2bSrkDGRvb8/27dvR09MjNTWVdu3akZOTQ0ZGBjo6Oujr6/PgwQM6d+4M5BlN6+np0a5dOx48eCCNk/95XVBSrAAZGRl8/vnn6OjoEBwcLJk1N+RYK0O7du04f/689PPDhw8bxLyLcuHCBWbPns38+fMZNWoUcXFx0gtl+F9c7dq1K/R5/vfUunVrnj17hlwuR01NrcrPof7/qhY0SpydnTl69KhkMQd5/ca1tbUZOnQoYWFhQF5/8v79+6OhocHQoUOlc8+fP4+WlhYdOnTAwsKC8PBw5HI5d+7c4fbt27Xm/FNRZDIZXbp0Yc2aNYU8VhtjrCXx3nvvERMTQ1paGi9evODIkSMVdnuqL9y/f5+ZM2fi7+/PqFGjgDzz7cTERO7cuYNcLiciIgILCws6duyIlpaWVPWzf/9+LCws0NDQoH///hw8eBCAsLCwKj0H0Y9bUC/o3r07CQkJAKSnp+Pp6cndu3dp3rw5/v7+GBgY8OrVK7y9vbl8+TKamposXbqUnj17olAoWLlyJadOnQLAy8uLwYMH12U4hbhy5QoODg507dpV2tvU09Pj+++/b3SxlkV4eDibNm0iOzubsWPH8umnn9b1lCrF0qVL2bt3r/RXEICrqytvvvkmy5cv59WrVwwdOhQvLy/+Xzt2aAVBCARRcDBrVxMDCSLw64iPBJA4ArgYTs57VRG0+qJLKbHWijFG3HujtRbf98XzPLH3jt57nHOi1hpzznjf968twg2QjKsEIBnhBkhGuAGSEW6AZIQbIBnhBkhGuAGSEW6AZH5myMtvN3G7qQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# feature coefficients\n",
    "coeffs = model.coef_\n",
    "\n",
    "# visualizing coefficients\n",
    "index = X_train.columns.tolist()\n",
    "\n",
    "(pd.DataFrame(coeffs, index = index, columns = ['coeff']).sort_values(by = 'coeff')\n",
    " .plot(kind='barh', figsize=(4,10)))\n",
    "\n",
    "# add this to regression notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter variables near zero coefficient value\n",
    "temp = pd.DataFrame(coeffs, index = index, columns = ['coeff']).sort_values(by = 'coeff')\n",
    "temp = temp[(temp['coeff']>1) | (temp['coeff']< -1)]\n",
    "\n",
    "# drop features\n",
    "cols_coeff = temp.index.to_list()\n",
    "X_train = X_train[cols_coeff]\n",
    "X_test = X_test[cols_coeff]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _p_ value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.951\n",
      "Model:                            OLS   Adj. R-squared:                  0.936\n",
      "Method:                 Least Squares   F-statistic:                     62.39\n",
      "Date:                Mon, 18 Apr 2022   Prob (F-statistic):           2.82e-76\n",
      "Time:                        22:31:09   Log-Likelihood:                -1727.5\n",
      "No. Observations:                 194   AIC:                             3549.\n",
      "Df Residuals:                     147   BIC:                             3703.\n",
      "Df Model:                          46                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "wheel-base                281.6439     71.285      3.951      0.000     140.768     422.520\n",
      "bore                      465.6075   1396.454      0.333      0.739   -2294.111    3225.326\n",
      "horsepower                 97.4677     18.432      5.288      0.000      61.041     133.894\n",
      "highway-mpg                49.4275     66.148      0.747      0.456     -81.296     180.151\n",
      "make_audi                 665.4536   2291.555      0.290      0.772   -3863.193    5194.100\n",
      "make_bmw                 4928.9462   2184.626      2.256      0.026     611.616    9246.277\n",
      "make_chevrolet          -4047.0002   2237.458     -1.809      0.073   -8468.740     374.739\n",
      "make_dodge              -3778.6720   1799.567     -2.100      0.037   -7335.036    -222.308\n",
      "make_honda              -1186.7246   2246.382     -0.528      0.598   -5626.099    3252.650\n",
      "make_isuzu              -4929.0743   2712.041     -1.817      0.071   -1.03e+04     430.551\n",
      "make_jaguar              8086.5240   2432.715      3.324      0.001    3278.912    1.29e+04\n",
      "make_mazda              -2276.5393   1757.349     -1.295      0.197   -5749.471    1196.392\n",
      "make_mercedes-benz       3863.6128   2651.200      1.457      0.147   -1375.778    9103.003\n",
      "make_mercury            -6789.2161   2856.436     -2.377      0.019   -1.24e+04   -1144.232\n",
      "make_mitsubishi         -3882.5735   1833.689     -2.117      0.036   -7506.371    -258.776\n",
      "make_nissan             -3179.6806   1686.183     -1.886      0.061   -6511.972     152.610\n",
      "make_peugot              2069.3643   1763.148      1.174      0.242   -1415.028    5553.757\n",
      "make_plymouth           -3941.6185   1813.579     -2.173      0.031   -7525.673    -357.564\n",
      "make_porsche             4750.4432   2837.617      1.674      0.096    -857.350    1.04e+04\n",
      "make_saab                 -19.1303   1900.566     -0.010      0.992   -3775.092    3736.831\n",
      "make_subaru             -2986.7875   1653.786     -1.806      0.073   -6255.055     281.480\n",
      "make_toyota             -3747.9903   1614.208     -2.322      0.022   -6938.042    -557.938\n",
      "make_volkswagen         -2441.5832   1761.095     -1.386      0.168   -5921.917    1038.750\n",
      "make_volvo              -1375.1368   1963.586     -0.700      0.485   -5255.641    2505.368\n",
      "fuel-type_gas             -1.2e+04   8417.086     -1.426      0.156   -2.86e+04    4630.539\n",
      "aspiration_turbo          818.9923    752.036      1.089      0.278    -667.206    2305.191\n",
      "num-of-doors_two         -337.3562    544.378     -0.620      0.536   -1413.174     738.462\n",
      "body-style_hardtop      -4080.8676   1284.060     -3.178      0.002   -6618.469   -1543.266\n",
      "body-style_hatchback    -4367.5343   1237.259     -3.530      0.001   -6812.648   -1922.421\n",
      "body-style_sedan        -4594.7805   1299.330     -3.536      0.001   -7162.560   -2027.001\n",
      "body-style_wagon        -4997.1786   1396.995     -3.577      0.000   -7757.968   -2236.390\n",
      "drive-wheels_fwd        -1527.3479    948.503     -1.610      0.109   -3401.812     347.116\n",
      "drive-wheels_rwd         -512.1274   1243.703     -0.412      0.681   -2969.975    1945.721\n",
      "engine-location_rear     4331.6865   2028.645      2.135      0.034     322.610    8340.763\n",
      "engine-type_l           -2940.3333   1538.921     -1.911      0.058   -5981.600     100.934\n",
      "engine-type_ohc          2382.4376   1040.789      2.289      0.023     325.596    4439.279\n",
      "engine-type_ohcf         1344.8990   1134.361      1.186      0.238    -896.863    3586.661\n",
      "engine-type_ohcv         -263.6340   1329.622     -0.198      0.843   -2891.278    2364.010\n",
      "num-of-cylinders_five   -1.073e+04   2287.713     -4.689      0.000   -1.52e+04   -6206.512\n",
      "num-of-cylinders_four   -1.253e+04   2863.313     -4.375      0.000   -1.82e+04   -6868.864\n",
      "num-of-cylinders_six    -9596.2807   2503.113     -3.834      0.000   -1.45e+04   -4649.546\n",
      "num-of-cylinders_three  -5009.6976   2654.457     -1.887      0.061   -1.03e+04     236.130\n",
      "num-of-cylinders_twelve -1.204e+04   4096.423     -2.939      0.004   -2.01e+04   -3943.184\n",
      "fuel-system_2bbl         1833.5127   1668.592      1.099      0.274   -1464.015    5131.040\n",
      "fuel-system_idi         -8813.9717   8598.155     -1.025      0.307   -2.58e+04    8177.988\n",
      "fuel-system_mfi           299.5928   2996.654      0.100      0.920   -5622.494    6221.680\n",
      "fuel-system_mpfi          910.8974   1747.280      0.521      0.603   -2542.136    4363.930\n",
      "fuel-system_spdi          355.9221   2084.590      0.171      0.865   -3763.714    4475.558\n",
      "fuel-system_spfi         4502.3426   3369.808      1.336      0.184   -2157.184    1.12e+04\n",
      "==============================================================================\n",
      "Omnibus:                       68.902   Durbin-Watson:                   1.503\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              328.913\n",
      "Skew:                           1.276   Prob(JB):                     3.78e-72\n",
      "Kurtosis:                       8.846   Cond. No.                     1.37e+16\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 2.29e-26. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "ols = sm.OLS(y, X).fit()\n",
    "print(ols.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variance Inflation Factor (VIF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DataS\\anaconda3\\lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:193: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  vif = 1. / (1. - r_squared_i)\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "vif = pd.Series([variance_inflation_factor(X.values, i) for i in range(X.shape[1])], index=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>engine-type_ohc</th>\n",
       "      <td>9.832494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style_wagon</th>\n",
       "      <td>9.788068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make_mitsubishi</th>\n",
       "      <td>9.725685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>9.344857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheel-base</th>\n",
       "      <td>8.932013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel-system_spdi</th>\n",
       "      <td>8.894106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make_bmw</th>\n",
       "      <td>8.729793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make_volkswagen</th>\n",
       "      <td>8.326544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make_mazda</th>\n",
       "      <td>8.291162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make_porsche</th>\n",
       "      <td>7.522600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make_audi</th>\n",
       "      <td>7.281424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bore</th>\n",
       "      <td>6.718156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make_dodge</th>\n",
       "      <td>5.923599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make_plymouth</th>\n",
       "      <td>5.292481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-type_ohcv</th>\n",
       "      <td>5.113583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make_saab</th>\n",
       "      <td>5.008663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make_jaguar</th>\n",
       "      <td>4.168538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aspiration_turbo</th>\n",
       "      <td>3.954427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make_chevrolet</th>\n",
       "      <td>3.526236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make_isuzu</th>\n",
       "      <td>3.471926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num-of-doors_two</th>\n",
       "      <td>3.334352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style_hardtop</th>\n",
       "      <td>3.015922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel-system_mfi</th>\n",
       "      <td>2.130479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         vif\n",
       "engine-type_ohc     9.832494\n",
       "body-style_wagon    9.788068\n",
       "make_mitsubishi     9.725685\n",
       "highway-mpg         9.344857\n",
       "wheel-base          8.932013\n",
       "fuel-system_spdi    8.894106\n",
       "make_bmw            8.729793\n",
       "make_volkswagen     8.326544\n",
       "make_mazda          8.291162\n",
       "make_porsche        7.522600\n",
       "make_audi           7.281424\n",
       "bore                6.718156\n",
       "make_dodge          5.923599\n",
       "make_plymouth       5.292481\n",
       "engine-type_ohcv    5.113583\n",
       "make_saab           5.008663\n",
       "make_jaguar         4.168538\n",
       "aspiration_turbo    3.954427\n",
       "make_chevrolet      3.526236\n",
       "make_isuzu          3.471926\n",
       "num-of-doors_two    3.334352\n",
       "body-style_hardtop  3.015922\n",
       "fuel-system_mfi     2.130479"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = X_train.columns.tolist()\n",
    "vif_df = pd.DataFrame(vif, index = index, columns = ['vif']).sort_values(by = 'vif', ascending=False)\n",
    "vif_df[vif_df['vif']<10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance/impurity based feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=200, random_state=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate model\n",
    "model = RandomForestClassifier(n_estimators=200, random_state=0)\n",
    "# fit model\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  feature importance\n",
    "importances = model.feature_importances_\n",
    "\n",
    "# visualization\n",
    "cols = X.columns\n",
    "(pd.DataFrame(importances, cols, columns = ['importance'])\n",
    " .sort_values(by='importance', ascending=True)\n",
    " .plot(kind='barh', figsize=(4,10))\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate standard deviation of feature importances \n",
    "std = np.std([i.feature_importances_ for i in model.estimators_], axis=0)\n",
    "\n",
    "# visualization\n",
    "feat_with_importance  = pd.Series(importances, X.columns)\n",
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "feat_with_importance.plot.bar(yerr=std, ax=ax)\n",
    "ax.set_title(\"Feature importances\")\n",
    "ax.set_ylabel(\"Mean decrease in impurity\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Automated feature selection with `sci-kit learn` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import modules\n",
    "from sklearn.feature_selection import (SelectKBest, chi2, SelectPercentile, SelectFromModel, \n",
    "                                       SequentialFeatureSelector, SequentialFeatureSelector)\n",
    "                                       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Chi-squared based technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select K best features\n",
    "X_best = SelectKBest(chi2, k=10).fit_transform(X,y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of best features\n",
    "X_best.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keep 75% top features \n",
    "X_top = SelectPercentile(chi2, percentile = 75).fit_transform(X,y)\n",
    "\n",
    "# number of best features\n",
    "X_top.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=0.002, dual=False, penalty='l1')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# implement algorithm\n",
    "from sklearn.svm import LinearSVC\n",
    "model = LinearSVC(penalty= 'l1', C = 0.002, dual=False)\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select features using the meta transformer\n",
    "selector = SelectFromModel(estimator = model, prefit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = selector.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['wheel-base', 'horsepower'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# names of selected features\n",
    "feature_names = np.array(X.columns)\n",
    "feature_names[selector.get_support()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c) Sequential selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate model\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select features\n",
    "selector = SequentialFeatureSelector(estimator=model, n_features_to_select=10, direction='backward', cv=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "selector.fit_transform(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bore', 'make_mitsubishi', 'make_nissan', 'make_saab',\n",
       "       'aspiration_turbo', 'num-of-doors_two', 'body-style_hatchback',\n",
       "       'engine-type_ohc', 'num-of-cylinders_twelve', 'fuel-system_spdi'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# names of features selected\n",
    "feature_names = np.array(X.columns)\n",
    "feature_names[selector.get_support()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Principal Compoenent Analysis (PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling data\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA()\n",
    "pca.fit(X_scaled)\n",
    "evr = pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cumulative explained variance')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the variance explained by each principal components\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(range(0, len(evr)), evr.cumsum(), marker=\"o\", linestyle=\"--\")\n",
    "\n",
    "plt.xlabel(\"Number of components\")\n",
    "plt.ylabel(\"Cumulative explained variance\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
